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Image quality assessment (IQA) is an important task of image processing and has diverse applications, such as image super-resolution reconstruction, image transmission and monitoring systems. This paper proposes a perceptual hashing algorithm with complementary color wavelet transform (CCWT) and compressed sensing (CS) for reduced-reference (RR) IQA. The CCWT is exploited to decompose input color image into different sub-bands. Since the calculation of CCWT uses all color channels without discarding any information, the distortions introduced by digital operations on color channels are preserved in the CCWT sub-bands. The block-based CS is used to extract features from the CCWT sub-bands. As the Euclidean distance between the block-based CS features is slightly influenced by content-preserving operations, perceptual features constructed by Euclidean distances are robust, discriminative and compact. Hash sequence is finally determined by quantifying the perceptual features. Effectiveness of the proposed hashing is verified by various experiments on four open image databases. Experimental results demonstrate that the proposed hashing is superior to some state-of-the-art algorithms in terms of classification and RR IQA application. [ABSTRACT FROM AUTHOR]

As two practical and important image processing tasks, color demosaicking (CDM) and super-resolution (SR) have been studied for decades. However, most literature studies these two tasks independently, ignoring the potential benefits of a joint solution. In this paper, aiming at efficient and effective joint demosaicking and super-resolution (JDSR), a well-designed two-stage convolutional neural network (CNN) architecture is proposed. For the first stage, by making use of the sampling-pattern information, a pattern-aware feature extraction (PFE) module extracts features directly from the Bayer-sampled low-resolution (LR) image, while keeping the resolution of the extracted features the same as the input. For the second stage, a dual-branch feature refinement (DFR) module effectively decomposes the features into two components with different spatial frequencies, on which different learning strategies are applied. On each branch of the DFR module, the feature refinement unit, namely, densely-connected dual-path enhancement blocks (DDEB), establishes a sophisticated nonlinear mapping from the LR space to the high-resolution (HR) space. To achieve strong representational power, two paths of transformations and the channel attention mechanism are adopted in DDEB. Extensive experiments demonstrate that the proposed method is superior to the sequential combination of state-of-the-art (SOTA) CDM and SR methods. Moreover, with much smaller model size, our approach also surpasses other SOTA JDSR methods. [ABSTRACT FROM AUTHOR]

High dynamic range (HDR) image, which has a powerful capacity to represent the wide dynamic range of real-world scenes, has been receiving attention from both academic and industrial communities. Although HDR imaging devices have become prevalent, the display devices for HDR images are still limited. To facilitate the visualization of HDR images in standard low dynamic range displays, many different tone mapping operators (TMOs) have been developed. To create a fair comparison of different TMOs, this paper proposes a BLInd QUality Evaluator to blindly predict the quality of Tone-Mapped Images (BLIQUE-TMI) without accessing the corresponding HDR versions. BLIQUE-TMI measures the quality of TMIs by considering the following aspects: 1) visual information; 2) local structure; and 3) naturalness. To be specific, quality-aware features related to the former two aspects are extracted in a local manner based on sparse representation, while quality-aware features related to the third aspect are derived based on global statistics modeling in both intensity and color domains. All the extracted local and global quality-aware features constitute a final feature vector. An emergent machine learning technique, i.e., extreme learning machine, is adopted to learn a quality predictor from feature space to quality space. The superiority of BLIQUE-TMI to several leading blind IQA metrics is well demonstrated on two benchmark databases. [ABSTRACT FROM AUTHOR]

Pedestrian detection is an important yet challenging task. In this paper, a pedestrian codetection framework for binocular stereo sequences is proposed. Binocular vision and consecutive frames can provide more information than a single image. That information can be used to improve detection performance by reducing the number of candidate detection windows with low confidence or enhancing the high-confidence candidates. To design the framework, we follow the intuition that a pedestrian has consistent appearance when observed from the same or different viewpoints. First, a baseline detector is used in both stereo images with a conservative threshold in order to expand the set of detection candidates. Before the detection process, an assisted stixel world model is computed for both left and right frames. Thus, the search range of the detector is greatly reduced with the help of the stixel world. Second, adjacency-constrained patch matching is proposed to build correspondence between two candidates in both intra and inter sequences of binocular vision. Finally, we establish a mechanism to update the score of the detection aided by the corresponding candidates. The experimental results show that our framework significantly improves the performance of the evaluated baseline pedestrian approaches. [ABSTRACT FROM AUTHOR]

We describe a methodology that is designed to match key point and region-based features in real-world images, acquired from long-running security cameras with no control over the environment. We detect frame duplication and images from static scenes that have no activity to prevent processing saliently identical images, and describe a novel blur-sensitive feature detection method, a combinatorial feature descriptor, and a distance calculation that efficiently unites texture and color attributes to discriminate feature correspondence in low-quality images. Our methods are tested by performing key point matching on real-world security images such as outdoor closed-circuit television videos that are low quality and acquired in uncontrolled conditions with visual distortions caused by weather, crowded scenes, emergency lighting, or the high angle of the camera mounting. We demonstrate an improvement in accuracy of matching key points between images compared with state-of-the-art feature descriptors. We use key point features from a Harris corner detector, scale-invariant feature transform, speeded-up robust features, binary robust invariant scalable keypoints, and features from accelerated segment test as well as MSER and MSCR region detectors to provide a comprehensive analysis of our generic method. We demonstrate feature matching using a 138D descriptor that improves the matching performance of a state-of-the-art 384D color descriptor with just 36% of the storage requirements. [ABSTRACT FROM PUBLISHER]

Person reidentification consists of recognizing individuals across different sensors of a camera network. Whereas clothing appearance cues are widely used, other modalities could be exploited as additional information sources, like anthropometric measures and gait. In this paper, we investigate whether the reidentification accuracy of clothing appearance descriptors can be improved by fusing them with anthropometric measures extracted from depth data, using RGB-D sensors, in unconstrained settings. We also propose a dissimilarity-based framework for building and fusing the multimodal descriptors of pedestrian images for reidentification tasks, as an alternative to the widely used score-level fusion. The experimental evaluation is carried out on two data sets including RGB-D data, one of which is a novel publicly available data set that we acquired using Kinect sensors. The fusion with anthropometric measures increases the first-rank recognition rate of clothing appearance descriptors up to 20%, whereas our fusion approach reduces the processing cost of the matching phase. [ABSTRACT FROM PUBLISHER]

Person identification across nonoverlapping cameras, also known as person reidentification, aims to match people at different times and locations. Reidentifying people is of great importance in crucial applications such as wide-area surveillance and visual tracking. Due to the appearance variations in pose, illumination, and occlusion in different camera views, person reidentification is inherently difficult. To address these challenges, a reference-based method is proposed for person reidentification across different cameras. Instead of directly matching people by their appearance, the matching is conducted in a reference space where the descriptor for a person is translated from the original color or texture descriptors to similarity measures between this person and the exemplars in the reference set. A subspace is first learned in which the correlations of the reference data from different cameras are maximized using regularized canonical correlation analysis (RCCA). For reidentification, the gallery data and the probe data are projected onto this RCCA subspace and the reference descriptors (RDs) of the gallery and probe are generated by computing the similarity between them and the reference data. The identity of a probe is determined by comparing the RD of the probe and the RDs of the gallery. A reranking step is added to further improve the results using a saliency-based matching scheme. Experiments on publicly available datasets show that the proposed method outperforms most of the state-of-the-art approaches. [ABSTRACT FROM PUBLISHER]

In this paper, we propose an efficient blind image quality assessment (BIQA) algorithm, which is characterized by a new feature fusion scheme and a k -nearest-neighbor (KNN)-based quality prediction model. Our goal is to predict the perceptual quality of an image without any prior information of its reference image and distortion type. Since the reference image is inaccessible in many applications, the BIQA is quite desirable in this context. In our method, a new feature fusion scheme is first introduced by combining an image’s statistical information from multiple domains (i.e., discrete cosine transform, wavelet, and spatial domains) and multiple color channels (i.e., Y, Cb, and Cr). Then, the predicted image quality is generated from a nonparametric model, which is referred to as the label transfer (LT). Based on the assumption that similar images share similar perceptual qualities, we implement the LT with an image retrieval procedure, where a query image’s KNNs are searched for from some annotated images. The weighted average of the KNN labels (e.g., difference mean opinion score or mean opinion score) is used as the predicted quality score. The proposed method is straightforward and computationally appealing. Experimental results on three publicly available databases (i.e., LIVE II, TID2008, and CSIQ) show that the proposed method is highly consistent with human perception and outperforms many representative BIQA metrics. [ABSTRACT FROM AUTHOR]

In this paper, we propose a method that is able to detect fires by analyzing videos acquired by surveillance cameras. Two main novelties have been introduced. First, complementary information, based on color, shape variation, and motion analysis, is combined by a multiexpert system. The main advantage deriving from this approach lies in the fact that the overall performance of the system significantly increases with a relatively small effort made by the designer. Second, a novel descriptor based on a bag-of-words approach has been proposed for representing motion. The proposed method has been tested on a very large dataset of fire videos acquired both in real environments and from the web. The obtained results confirm a consistent reduction in the number of false positives, without paying in terms of accuracy or renouncing the possibility to run the system on embedded platforms. [ABSTRACT FROM PUBLISHER]

Every year, a large number of wildfires all over the world burn forested lands, causing adverse ecological, economic, and social impacts. Beyond taking precautionary measures, early warning and immediate response are the only ways to avoid great losses. To this end, in this paper we propose a computer vision approach for fire-flame detection to be used by an early-warning fire monitoring system. Initially, candidate fire regions in a frame are defined using background subtraction and color analysis based on a nonparametric model. Subsequently, the fire behavior is modeled by employing various spatio-temporal features, such as color probability, flickering, spatial, and spatio-temporal energy, while dynamic texture analysis is applied in each candidate region using linear dynamical systems and a bag-of-systems approach. To increase the robustness of the algorithm, the spatio-temporal consistency energy of each candidate fire region is estimated by exploiting prior knowledge about the possible existence of fire in neighboring blocks from the current and previous video frames. As a final step, a two-class support vector machine classifier is used to classify the candidate regions. Experimental results have shown that the proposed method outperforms existing state-of-the-art algorithms. [ABSTRACT FROM PUBLISHER]

Networks of smart cameras share large amounts of data to accomplish tasks such as reidentification. We propose a feature-selection method that minimizes the data needed to represent the appearance of objects by learning the most appropriate feature set for the task at hand (person reidentification). The computational cost for feature extraction and the cost for storing the feature descriptor are considered jointly with feature performance to select cost-effective good features. This selection allows us to improve intercamera reidentification while reducing the bandwidth that is necessary to share data across the camera network. We also rank the selected features in the order of effectiveness for the task to enable a further reduction of the feature set by dropping the least effective features when application constraints require this adaptation. We compare the proposed approach with state-of-the-art methods on the iLIDS and VIPeR datasets and show that the proposed approach considerably reduces network traffic due to intercamera feature sharing while keeping the reidentification performance at an equivalent or better level compared with the state of the art. [ABSTRACT FROM AUTHOR]