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Robust vision-based hand pose estimation is highly sought but still remains a challenging task, due to its inherent difficulty partially caused by self-occlusion among hand fingers. In this paper, an innovative framework for real-time static hand gesture recognition is introduced, based on an optimized shape representation build from multiple shape cues. The framework incorporates a specific module for hand pose estimation based on depth map data, where the hand silhouette is first extracted from the extremely detailed and accurate depth map captured by a time-of-flight (ToF) depth sensor. A hybrid multi-modal descriptor that integrates multiple affine-invariant boundary-based and region-based features is created from the hand silhouette to obtain a reliable and representative description of individual gestures. Finally, an ensemble of one-vs.-all support vector machines (SVMs) is independently trained on each of these learned feature representations to perform gesture classification. When evaluated on a publicly available dataset incorporating a relatively large and diverse collection of egocentric hand gestures, the approach yields encouraging results that agree very favorably with those reported in the literature, while maintaining real-time operation.

Tracking tools for target structures in ultrasound imaging are a crucial point in the development of semiautonomous image guided procedures. This paper presents an algorithm for semi-autonomous target-structure tracking, which uses a parametric active contour model with a Fourier descriptor formulation. The algorithm is tested in a soft real-time scenario, and its robustness is analysed w.r.t. probe translation and rotation, including also target structure deformations. In terms of temporal performance, the algorithm implemented in MATLAB has shown the ability to operate easily at 25 fps when coupled with an ultrasound probe, showing however capability to reach 40 fps when tested as a standalone algorithm.

This paper attempts to design an automated, efficient and intelligent tomato grading method that facilitates the graded selling of the fruit. Based on machine vision, the color images of tomatoes with different morphologies were studied, and the color, shape and size were selected as the key features. On this basis, an automated grading classifier was created based on the surface features of tomatoes, and a grading platform was set up to verify the effect of the classifier. Specifically, the Hue value distributions of tomatoes with different maturities were investigated, and the Hue value ranges were determined for mature, semi-mature and immature tomatoes, producing the color classifier. Next, the first-order Fourier descriptor (1D- FD) was adopted to describe the radius sequence of tomato contour, and an equation was established to compute the irregularity of tomato contour, creating the shape classifier. After that, a linear regression equation was constructed to reflect the relationship between the transverse diameters of actual tomatoes and tomato images, and a classifier between large, medium and small tomatoes was produced based on the transverse diameter. Finally, a comprehensive tomato classifier was built based on the color, shape and size diameters. The experimental results show that the mean grading accuracy of the proposed method was 90.7%. This means our method can achieve automated real-time grading of tomatoes.

The emergence of civil-use depth sensors made vision-based man-machine interaction possible through hand gesture, which relies fundamentally on hand pose estimation techniques. In this work, a novel hand pose estimation method is proposed, based essentially on Fourier-descriptor-based indexing in a pre-built hand template library. The proposed method takes color-depth as input, extracts necessary hand information in preprocessing, and estimate hand pose through two stages, from rough to accurate. This proposed hand pose estimation method is simple and efficient; quantitative and qualitative evaluations exhibit its effectiveness.

International audience; With increasing number of Unmanned Aerial Vehicles (UAVs) -also known as drones- in our lives, safety and privacy concerns have arose. Especially, strategic locations such as governmental buildings, nuclear power stations etc. are under direct threat of these publicly available and easily accessible gadgets. Various methods are proposed as counter-measure, such as acoustics based detection, RF signal interception, micro-doppler RADAR etc. Computer vision based approach for detecting these threats seems as a viable solution due to various advantages. We envision an autonomous drone detection and tracking system for the protection of strategic locations. In this work, 2-dimensional scale, rotation and translation invariant Generic Fourier Descriptor (GFD) features (which are analyzed with a neural network) are used for classifying aerial targets as a drone or bird. For the training of this system, a large dataset composed of birds and drones is gathered from open sources. We have achieved up to 85.3% overall correct classification rate.

Automatic grading of Harumanis mangoes by its shape and weight analysis.The Fourier descriptor method was developed to grade the shape of Harumanis mango.The cylinder method was developed to grade the weight of Harumanis mango.Multi-classification using Support Vector Machine and Discriminant analysis. This paper presents the work on the use of visible imaging as a tool in grading the mangoes. A Fourier-descriptor method was applied on mango images acquired by a CCD camera, to grade the fruits by their shapes. The method was able to correctly classify 98.3% using DA and 100% using SVM. It is also possible to estimate the weight of the mangoes from their images by applying the Cylinder approximation analysis method. The scatter plot between the estimated and actual values of the weight shows high correlation, with R2 equal to 94.0%. The high prediction accuracy obtained shows that this simple formula is adequate for the prediction of fruit weight and volume (measured volume using the cylinder method). The correlation formula derived based on the collected data is determined as w=2.256V-157.7 where w is estimated weight in grams and V is estimated volume. Overall result for weight grading using our proposed method yields 95% accuracy.

This paper presents a gait recognition method which is based on spatio-temporal movement characteristics of human subject with respect to surveillance camera. Different measures, like leg rise from ground (LRFG), the angles created between the legs with the centre of Mass (ABLC), angles created between the Centre of Mass Knee and Ankle with the (CKA), angles created between Centre of Mass, Wrist and knee (CWK), the distances between the control points and centre of Mass (DCC) have been taken as different features. Fourier descriptor has been used for shape extraction of individual frames of a subject. Statistical approach has been used for recognition of individuals based on the n feature vectors, each of size 23(collected from LRFG, ABLCs, CKA, CWK and DCCs) for each video frame. It has been found that recognition result of our approach is encouraging with compared to other recent methods.

Automatic orange quality classification based on computer image processing is accurate and efficient. In this paper, we discuss the orange feature extraction and description method based on image processing. Design an orange image edge detection method based on Canny operator, color characteristics description methods based on HIS model and shape characteristics description methods based on Fourier descriptor operator. The experiment result proof that Canny operator is high SNR,high accuracy and low computation;HIS model is more accord with human vision and low computation also; shape characteristics description methods based on Fourier descriptor operator is more easy to shape classification.

Person re-identification in public areas (such as airports, train stations and shopping malls) has recently received increased attention within computer vision research due, in part, to the demand for enhanced levels of security. Re-identifying subjects within non-overlapped camera networks can be considered as a challenging task. Illumination changes in different scenes, variations in camera resolutions, field of view and human natural motion are the key obstacles to accurate implementation. This study assesses the use of Generic Fourier Shape Descriptors (GFD) on person silhouettes for re-identification and furthermore identifies which sections of a subjects' silhouette is able to deliver optimum performance. Human silhouettes of 90 subjects from the CASIA dataset walking 0° and 90° to a fixed CCTV camera were used for the purpose of re-identification. Each subject's video sequence comprised between 10 and 50 frames. For both views, silhouettes were segmented into eight algorithmically-defined areas: head and neck, shoulders, upper 50%, lower 50%, upper 15%, middle 35%, lower 40% and whole body. A GFD was used independently on each segment at each angle. After extracting the GFD feature for each frame, a linear discriminant analysis (LDA) classifier was used to investigate re-identification accuracy rate, where 50% of each subject's frames were used for training and the other 50% were used for testing. The results show that 97% identification accuracy rate at the 10th rank is achieved by using GFD on the upper 50% segment of the human silhouette front (0°) side. For 90° images, using GFD on the upper 15% silhouette segment resulted in almost 98% accuracy rate at the 10th rank.

Sign languages are used all over the world as a primary means of communication by deaf people. Sign language translation is a promising application for vision-based gesture recognition methods. Therefore, it is need such a tool that can translate sign language directly. This paper aims to create a system that can translate static sign language into textual form automatically based on computer vision. The method contains three phases, i.e. segmentation, feature extraction, and recognition. We used Generic Fourier Descriptor (GFD) as feature extraction method and K-Nearest Neighbour (KNN) as classification approach to recognize the signs. The system was applied to recognize each 120 stored images in database and 120 images which is captured real time by webcam. We also translated 5 words in video sequences. The experiment revealed that the system can recognized the signs with about 86 % accuracy for stored images in database and 69 % for testing data which is captured real time by webcam.

Indian Sign Language (ISL) interpretation is the major research work going on to aid Indian deaf and dumb people. Considering the limitation of glove/sensor based approach, vision based approach was considered for ISL interpretation system. Among different human modalities, hand is the primarily used modality to any sign language interpretation system so, hand gesture was used for recognition of manual alphabets and numbers. ISL consists of manual alphabets, numbers as well as large set of vocabulary with grammar. In this paper, methodology for recognition of static ISL manual alphabets, number and static symbols is given. ISL alphabet consists of single handed and two handed sign. Fourier descriptor as a feature extraction method was chosen due the property of invariant to rotation, scale and translation. True positive rate was achieved 94.15% using nearest neighbourhood classifier with Euclidean distance where sample data were considered with different illumination changes, different skin color and varying distance from camera to signer position.&nbsp

In this chapter, a mobile system is proposed to aid doctors and general users in monitoring skin cancer. In this system, image retrieval techniques are used to help users find similar skin cancer cases in a database. The main idea of the system is to use shape features of a query image taken by a smartphone or uploaded from other resources to perform content-based image retrieval. In the system, the shape features of an image are first extracted by the skin lesion extraction method proposed in Ref. 1, then the features are used to match the skin lesions in the database. The features in the proposed system are obtained by Fourier descriptor. We developed an iPhone application with which users can compare the skin lesions detected by an iPhone to the skin lesion cases stored in a database and potentially detect malignant skin lesions early on in their development.

Airborne pollen is the main cause of pollen allergy, so statistic of the amount and distribution of pollen in the air is important. This paper presents a method to identify airborne pollen grains in optical microscopy images. After pollen region is segmented by thresholding, global shape descriptor and Fourier descriptor are used to extract shape features, gray level co-occurrence matrix is employed to extract texture features, and finally pollen grains are classified by a k-nearest neighborhood classifier. In the experiment with 55 cases ofPoaceae, 44 cases ofMoraceaeand 48 cases ofPinaceae, a classification rate of 82.31% can be obtained with an accuracy of 85.45%, 61.36%, and 97.91% for each family.

Shape descriptors have demonstrated encouraging potential for retrieving images based on image content, and a number of them have been reported in the literature. Nevertheless, most of the reported descriptors are still face accuracy and computational challenges. Fourier descriptors are considered to be promising descriptors as they are based on a sound theoretical foundation and also have the advantages of computational efficiency and attractive invariance properties. This paper proposes a new curvature-based Fourier descriptor (CBFD) for shape retrieval. The proposed descriptor takes an unconventional view of the curvature-scale-space representation of a shape contour as it treats it as a 2-D binary image (hence referred to as curvature-scale image, or CSI). The invariant descriptor is derived from the 2-D Fourier transform of the curvature-scale image. This method allows the descriptor to capture the detailed dynamics of the shape curvature and enhance the efficiency of the shape-matching process. Experiments using the widely known MPEG-7 databases in conjunction with a created noisy database have been conducted in order to compare the performance of the proposed descriptor with six commonly used shape-retrieval descriptors: curvature-scale-space descriptor (CSSD), angular radial transform descriptors (ARTD), Zernike moment descriptors (ZMD), radial Tchebichef moment descriptors (RTMD), generic Fourier descriptor (GFD), and the 1-D Fourier descriptor (1-FD). The performance of the proposed descriptor has surpassed that of many of these notable descriptors.

Hand is a highly variable organ and hand features are easily affected by environmental factors. Considering the characteristics of hand gesture, a novel hand gesture recognition algorithm based on hybrid moments is presented. First, According to the color cue, the hand shape is available to extract from the complicated background, then the contour moment invariant and Fourier Descriptor are extracted and fused into a hybrid feature, finally the hybrid feature are put into the BP network to identity. The experimental results show that the method has better robustness and higher recognition rate.

In object class recognition, the state-of-the-art works shows using combination varies local features may produce a good performance in recognition. These local features may have a different performance on one category to other category which it depends on the richness of local features. Due to that limitation, the shape features of objects are taken into consideration to be combined with local features. In this paper, we use Fourier Descriptor (FD), Elliptical Fourier Descriptors (EFD) and Moment Invariant (MI) as a global shape feature and Scale Invariant Feature Transform (SIFT) as local features. For learning technique, boosting is used in improving the recognition objects. This approach identifies the correct and misclassified dataset iteratively. Experimental results indicate that the recognition model outperform improved the accuracy of classification by up to 10% that is comparable to or better than that of state-of-the-art approaches.

Most of the shape recognition measures are rotation, translation and scale (RST) invariant. However, when the shape aspect ratio is changed or the mirror reflection of the object is considered for retrieval, some of these measures are not effectively useful. In consideration of the aforesaid issues, a Fourier descriptor based 'aspect ratio' and 'mirror reflection' invariant shape matching algorithm is presented here. This algorithm is also RST invariant.

Abstrct. There is a theoretical and experimental evidence that Fourier analysis is a powerful tool for tackling image processing problems. Its usefulness has been proven through its successful application in several fields. This paper describes the application of a new Fourier descriptor for outlining geographical profiles to be used in reconstruction and recognition.

Monitoring system has become one of the most important means for perimeter intrusion prevention. But most of existing monitoring systems are passive surveillance. In this paper, we propose a method to implement active perimeter intrusion detection by identifying human targets in video images captured by monitoring system. In order to enhance the robustness of detecting postures of human targets, this paper introduces Fourier Descriptor (FD) and Histogram of Oriented Gradients (HOG) to realize an effective detection of human bodies with multiple postures captured by fixed cameras. The experiment results confirm that the proposed algorithm has higher recognition rate for detecting human targets with walking, climbing, and jumping postures, and sufficiently meets the requirements of perimeter intrusion detection.

International audience; Recognizing objects using computational methods have become a popular research endeavor among researchers. In this paper, the recognition of fish based on GCFD (Generalized Color Fourier Descriptor) is introduced. The features are extracted using the GCFD technique which represents the image in a frequency domain. By analyzing the frequencies, the non-related information (backgrounds, not required lines or borders) are identified by performing some spectrum changes on the frequencies. The required objects in this study are the fish. In other words, the frequencies corresponding to the fish are maintained while other frequencies are removed from the frequency domain. After removing the non-related frequencies, the frequency domain is inversed in order to obtain the required image for further image processing. GCFD is used as a descriptor to extract the features of the fish as it is invariant to rotation and translation. A cultured fish tank installed with high-end video cameras is required to record the video from side and top views. Koi fish are chosen due to their active swimming behavior, variety of colors and easy-to-adapt habitat in the water. The evaluation of the technique is based on Bhattacharyya Distance. Some improvements were obtained in the recognition rate using the GCFD compared with existing color descriptors. The improvement can lead to better classifications of objects.

Shape-based image recognition is a key technology in computer vision, and Fourier descriptor (FD) is one important way to describe such images. FD uses the Fourier Transform of the contour coordinate as eigenvector to describe the image contour property. However, it can only show the contour property, but may fail to distinguish images with the same contour but different content. And the length of FD varies with the size of the image. In this paper, a modified Fourier descriptor (MFD) is proposed, which is invariant with translation, rotation, and scaling of the image. It takes both the contour property and the content into consideration. A 2D shape-based image can be represented by a one-dimensional discrete array with constant length, which makes it convenient to recognize different images. To prove the efficiency of the proposed algorithm, we have applied it to shape recognition experiments and got reasonable results.

In this paper, we present a computer vision based system able to detect human falls. We show in detail all the stages of our system and the considerations taken for the provided results. We propose a simple scheme for detection and tracking followed by a Finite State Machine (FSM). The proposed system presents a good performance under different environment conditions.

This paper considers theoretical prerequisites for a sensor or sensor system that provides data for reliable single plant detection. The goal of the paper is to close the gap between state of the art sensor technology and the theoretical “ideal” sensor to fulfill one and only purpose: providing single plant detection data in cluttered and disordered environments. Through analysis of artificial leaf forms, the paper determines expectations of the sensor spatial resolution and its maximum noise level, and is supported by the results of a theoretical primer on predefined (artificial) shape analysis. Each shape is analyzed by elliptic Fourier descriptor invariants obtained from the shape boundary, which allow translation, rotation and scale invariant of each object. This paper analyzes the statistical significance of each descriptor and its separability from other features. Finally an approximation formula developed here can be used to compute required resolution of such sensor for the plant detection.

This paper describes a rose variety recognition program and proposes Modified Fourier Descriptor . Based on the modified descriptor, a new measurement, Angle Measurement , is proposed for image shape analysis. The rationality of this measurement is analyzed and its mathematical definition is given. We use it for rose flower shape analysis and the experiment results show that this measurement is quite efficient for image shape analysis.

Shape is an essential visual feature of an image and it is widely used to describe image content in image classification and retrieval. In this paper, two new Fourier-based approaches for contour-based shape description are presented. These approaches present Fourier descriptors in multiple scales, which improves the shape classification and retrieval accuracy. The proposed methods outperform ordinary Fourier descriptors in the retrieval of complicated shapes without increasing computational cost.

Image databases have been a significant part of systems or applications in many fields including education, forensics and medical sciences. However, due to the increase in the number of digital resources especially images, the demand to manage these databases such as storing and retrieving the images effectively and efficiently from these databases is crucial. Content Based Image Retrieval (CBIR) is one of the best techniques used to retrieve similar images from the image database. Even though it is a good and well-researched technique, still there are some issues to be improved. One of the challenges in CBIR is to represent the image as a feature vector. In this paper, we proposed a new feature vector using Generic Fourier Descriptor (GFD) and Color Layout Descriptor (CLD) to increase the accuracy of the retrieval process. We tested our technique with the Coral Database and compared the results with other CBIR techniques. Proposed method achieved better results than previous techniques. The proposed technique can be used by the forensic department for identification of criminal suspect using forensic database.

Shape is one of the primary low-level image features exploited in the newly emerged content-based image retrieval (CBIR). Many shape methods exist. Among these shape methods, Fourier descriptor (FD) is one of the most widely used shape descriptors due to its simple computation, clarity and coarse to fine description capability. FD has been applied to a variety of applications, including image retrieval application. Generally, FD can be acquired in a number of ways, however, FD acquired from different ways can have different retrieval performance. In this paper, we study shape retrieval using FD. Specifically, we study different ways of acquiring FD, the number of FD features needed for general shape description and the retrieval performance of different FD. A Java client–server retrieval framework has been developed to facilitate the study. The retrieval performance of the different FD is tested using a standard shape database and a commonly used performance measurement.

Shape is an important image feature-it is one of the primary low level image features exploited in content-based image retrieval (CBIR). There are generally two types of shape descriptors in the literature: contour-based and region-based. In MPEG-7, the curvature scale space descriptor (CSSD) and Zernike moment descriptor (ZMD) have been adopted as the contour-based shape descriptor and region-based shape descriptor, respectively. In this paper, the two shape descriptors are evaluated against other shape descriptors, and the two shape descriptors are also evaluated against each other. Standard methodology is used in the evaluation. Specifically, we use standard databases, large data sets and query sets, commonly used performance measurement and guided principles. A Java-based client-server retrieval framework has been implemented to facilitate the evaluation. Results show that Fourier descriptor (FD) outperforms CSSD, and that CSSD can be replaced by either FD or ZMD.

Shape description is one of the key parts of image content description for image retrieval. Most of the existing shape descriptors are usually either application dependent or non-robust, making them undesirable for generic shape description. In this paper, a generic Fourier descriptor (GFD) is proposed to overcome the drawbacks of existing shape representation techniques. The proposed shape descriptor is derived by applying two-dimensional Fourier transform on a polar-raster sampled shape image. The acquired shape descriptor is application independent and robust. Experimental results show that the proposed GFD outperforms common contour-based and region-based shape descriptors.

Best way to localize iris inside an image of an eye is still a huge challenge because a standard regarding iris image does not yet exist. The first step of iris segmentation is iris localization. It is very important step because it will ensure the other step is running well. In this paper, a novel method to localize iris in a robust and simple manner is explained. The method is able to localize iris occluded by eyelids. Experiments on publicly available iris database that made by Malaysia Multimedia University Iris Database (MMU1) [1] showed a satisfying result of the proposed method.

A feature extraction method based on Wavelet transform is proposed. The proposed method is compared to Fourier descriptor. The experimental results with a ADEOS (ADvanced Earth Observing Satellite) / AVNIR (Advanced Visible and Near-Infrared Radiometer) image support an effectiveness of the proposed method.

There is strong interest in fast, accurate automated examination and detection of pathologies in vocal-fold function. Such systems could assist in detecting abnormalities during screenings, monitor treatment progress, and compare results after therapy or surgical intervention. It has been shown that analysis of the vocal-fold area as a function of time can be beneficial in the detection and diagnosis of pathologies related to control of sustained tone production. However, this method is less effective in detecting asymmetric function due to paralysis or obstruction in the laryngeal area. Here, we present an alternative approach to aid the diagnosis of asymmetric vocal-fold motion.

International audience; Abstract--In the area of monitoring and observation by sea, land or air by the use of information is an important task for the decision. We are interested in the present work on the information processing for the recognition of radar targets from ISAR images. To provide the information necessary for the recognition phase, we propose an approach radar image processing based on the combination algorithms Fisher and watershed. This combination allows us to obtain closed shapes (edge). Then, we proceed to a shape modeling based on the Fourier descriptor. Finally, we use the KNN classifier to evaluate the performance of our approach. The obtained results seem good.

We present a new Video Fire Detection (VFD) system for surveillance applications in fire and security industries. The system consists of three modules: pixel-level processing to identify potential fire blobs, blob-based spatial-temporal feature extraction, and a Support Vector Machine (SVM) classifier. The proposed novel spatial-temporal features include a spatial-temporal structural feature and a spatial-temporal contour dynamics feature. The spatial-temporal structural features are extracted from an accumulated motion mask (AMM) and an accumulated intensity template (AIT), capturing the concentric ring structure of fire intensity. The spatial-temporal dynamics features are based on the Fourier descriptor of contours in space and time, capturing the dynamic properties of fire. These global blob-based features are more robust and effective in rejecting false alarms and nuisance sources than pixel-wise features. In addition, extraction of the spatial-temporal features is very efficient, and no tracking of blobs or contours is needed. We also present a new multi-spectrum fire video database for algorithm testing. We evaluate the effectiveness of the proposed features on fire detection on the video database and obtain very promising results.

Instead of considering only the amount of fluorescent signal spatially distributed on the image of milled rice grains this paper shows how our single-wavelength spectral-imaging-based Thai jasmine (KDML105) rice identification system can be improved by analyzing the shape and size of the image of each milled rice variety especially during the image threshold operation. The image of each milled rice variety is expressed as chain codes and elliptic Fourier coefficients. After that, a feed-forward back-propagation neural network model is applied, resulting in an improved average FAR of 11.0% and FRR of 19.0% in identifying KDML105 milled rice from the unwanted four milled rice varieties.

Since contours often contain many different sized structures they need to be described at multiple scales. Rather than describe a contour at all scales it is more efficient to identify the most significant scales that best represent the structures present. These natural scales are determined by examining the Fourier descriptors of the contour and searching for significant bands. These correspond to bandwidths required to extract features at qualitatively significant resolution. Examples are given of using the determined scales in both low pass and high pass filters to eliminate fine and coarse detail.

The droplet shape in the process of droplet dropping can embodies the fundamental intrinsic properties of droplet, which can be used to identify the characteristics of liquid types. A new method is proposed by using Moment characters- Fourier descriptor to analysis the information of droplet dropping image. Data of the droplet profile changing is collected through the CCD camera. Moment characters sequence is calculated to represent the shape. The sequence is given Fourier counterchanges and normalized. The feature descriptor carries the liquid image features and unique information of liquid.

In many industrial applications, Fourier descriptors are commonly used when the description of the object shape is an important characteristic of the image. However, these descriptors are limited to single objects. We propose a general Fourier-based approach, called statistical Fourier descriptor (SFD), which computes shape statistics in grey level images. The SFD is computationally efficient and can be used for defect image classification. In a first example, we deployed the SFD to the inspection of welding seams with promising results.

In order to meet the requirements of users, a content-based image retrieval algorithm for electronic commerce has been investigated combining color features with shape features. The algorithm of image retrieval using both the invariant moment and the Fourier descriptor is proposed to retrieve the shape features of image. The secondary retrieval has been done in succession by use of modified color histogram. To avoid the influence of background, a method of image background elimination is proposed prior to the image retrieval using multi-features. Experimental results show that the algorithm is accuracy. The methods of garment image retrieval based on multi-features are promising to be useful for applications of online shopping.

The general shape analysis is a problem similar to the recognition or retrieval of shapes. The most important difference is that the processed object does not have to belong to a base class, but usually is only similar to the template representing the class. The most general information about a shape is here concluded, i.e. how round, elliptical, triangular, etc. it is. Such a problem can occur in applications with few general base classes, e.g. in pre-classification or the assignment of stamps extracted from an image to few classes in order to find the fraudulent stamp images (mainly governmental, official ones). In the paper seven shape descriptors were explored using the template matching approach. In order to select the best approach their performance was compared with results provided by almost two hundred humans and collected using appropriate inquiry forms.

Shape representation provides fundamental features formany applications in computer vision and it is known to be important cues for human vision. This paper presents an experimental study on recognition of mice behavior. We investigate the performance of the four shape recognition methods, namely Chain-Code, Curvature, Fourier descriptors and Zernike moments. These methods are applied to a real database that consists of four mice behaviors. Our experiments show that Zernike moments and Fourier descriptors provide the best results. To evaluate the noise tolerance, we corrupt each contour with different levels of noise. In this scenario, Fourier descriptor shows invariance to high levels of noise.