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Autoimmune diseases (AD) are the abnormal response of the immune system of the body to healthy tissues. ADs have generally been on the increase. Efficient computer aided diagnosis of ADs through classification of the human epithelial type 2 (HEp-2) cells become beneficial. These methods make lower diagnosis costs, faster response and better diagnosis repeatability. In this paper, we present an automated HEp-2 cell image classification technique that exploits the sparse coding of the visual features together with the Bag of Words model (SBoW). In particular, SURF (Speeded Up Robust Features) and SIFT (Scale-invariant feature transform) features are specially integrated to work in a complementary fashion. This method helps greatly improve the cell classification accuracy. Additionally, a hierarchical max-pooling method is proposed to aggregate the local sparse codes in different layers to provide final feature vector. Furthermore, various parameters of the dictionary learning including the dictionary size, the learning iteration number, and the pooling strategy is also investigated. Experiments conducted on publicly available datasets show that the proposed technique clearly outperforms state-of-the-art techniques in cell and specimen levels.

In this paper, we introduce a dictionary learning based approach applied to the problem of real-time reconstruction of MR image sequences that are highly undersampled in k-space. Unlike traditional dictionary learning, our method integrates both global and patch-wise (local) sparsity information and incorporates some priori information into the reconstruction process. Moreover, we use a Dependent Hierarchical Beta-process as the prior for the group-based dictionary learning, which adaptively infers the dictionary size and the sparsity of each patch; and also ensures that similar patches are manifested in terms of similar dictionary atoms. An efficient numerical algorithm based on the alternating direction method of multipliers (ADMM) is also presented. Through extensive experimental results we show that our proposed method achieves superior reconstruction quality, compared to the other state-of-the- art DL-based methods.

In this paper, we present a novel framework, called learning by propagability, for two essential data mining tasks, i.e., classification and regression. The whole learning process is driven by the philosophy that the data labels and the optimal feature representation jointly constitute a harmonic system, where the data labels are invariant with respect to the propagation on the similarity graph constructed based on the optimal feature representation. Based on this philosophy, a unified framework of learning by propagability is proposed for the purposes of both classification and regression. Specifically, this framework has three characteristics: 1) the formulation unifies the label propagation and optimal feature representation pursuing, and thus the label propagation process is enhanced by benefiting from the refined similarity graph constructed with the derived optimal feature representation instead of the original representation; 2) it unifies the formulations for supervised and semisupervised learning in both classification and regression tasks; and 3) it can directly deal with the multiclass classification problems. Extensive experiments for the classification task on UCI toy data sets, handwritten digit recognition, face recognition, and microarray recognition as well as for the regression task of human age estimation on the FG-NET aging database, all validate the effectiveness of our proposed learning framework, compared with the state-of-the-art counterparts.

A novel segmentation-based image approximation and coding technique is proposed. A hybrid quad-binary (QB) tree structure is utilized to efficiently model and code geometrical information within images. Compared to other tree-based representation such as wedgelets, the proposed QB-tree based method is more efficient for a wide range of contour features such as junctions, corners and ridges, especially at low bit rates.

This paper introduces the 3D color set partitioning in hierarchical trees (3D-CSPIHT) low bit rate embedded video coding scheme. The codec exploits the correlation between temporal and spatial wavelet coefficients and the interdependency between luminance and chrominance components to code color video sequences without the need for explicit bit allocation. Besides offering rate scalability, the new codec also produces multi-resolution scalable code streams. The hierarchical variable size block matching motion estimation technique is also integrated to demonstrate the motion estimation option with 3D-CSPIHT. The coding results show that 3D-CSPIHT produces better performance and visual quality compared to 3D-SPIHT.

In this paper, we present a hybrid framework for articulated 3-D human motion tracking from multiple synchronized cameras with potential uses in surveillance systems. Although the recovery of 3-D motion provides richer information for event understanding, existing methods based on either deterministic search or stochastic sampling lack robustness or efficiency. We therefore propose a hybrid sample-and-refine framework that combines both stochastic sampling and deterministic optimization to achieve a good compromise between efficiency and robustness. Similar motion patterns are used to learn a compact low-dimensional representation of the motion statistics. Sampling in a low-dimensional space is implemented during tracking, which reduces the number of particles drastically. We also incorporate a local optimization method based on simulated physical force/moment into our framework, which further improves the optimality of the tracking. Experimental results on several real human motion sequences show the accuracy and robustness of our method, which also has a higher sampling efficiency than most particle filtering-based methods.

We apply the probability hypothesis density (PHD) filter to track a random number of pedestrians in image sequences. The PHD filter is implemented using particle filter. How to design importance functions of the particle PHD filter remains a challenge, especially when targets can appear, disappear, merge, or split at any time. To meet this challenge, we have modeled the targets into two categories: survival objects and spontaneous birth objects. Based on the model, we have derived the data-driven importance function for a particle PHD filter and applied to pedestrians tracking where people or groups appear, merge, split, and disappear in the field of view of a camera. The experimental results have demonstrated the effectiveness of the particle PHD filter using the proposed importance function in tracking random number of pedestrians and deriving their locations.

In signal approximation, classical wavelet synthesis are known to produce Gibbs-like phenomenon around discontinuities when wavelet coefficients in the cone of influence of the discontinuities are quantized. By analyzing a function in a piecewise manner, filtering across discontinuities can be avoided. Using this principle, the interval wavelet transform can generate sparser representations in the vicinity of discontinuities than classical wavelet transforms. This work introduces two new constructions of interval wavelets and shows how they can be used for image compression and upscaling.

Just-noticeable distortion (JND), which refers to the maximum distortion that the human visual system (HVS) cannot perceive, plays an important role in perceptual image and video processing. In comparison with JND estimation for images, estimation of the JND profile for video needs to take into account the temporal HVS properties in addition to the spatial properties. In this paper, we develop a spatio-temporal model estimating JND in the discrete cosine transform domain. The proposed model incorporates the spatio-temporal contrast sensitivity function, the influence of eye movements, luminance adaptation, and contrast masking to be more consistent with human perception. It is capable of yielding JNDs for both still images and video with significant motion. The experiments conducted in this study have demonstrated that the JND values estimated for video sequences with moving objects by the model are in line with the HVS perception. The accurate JND estimation of the video towards the actual visibility bounds can be translated into resource savings (e.g., for bandwidth/storage or computation) and performance improvement in video coding and other visual processing tasks (such as perceptual quality evaluation, visual signal restoration/enhancement, watermarking, authentication, and error protection)

In this paper we present a pipelined hardware implementation of in-loop deblocking filter in H.264/AVC. A pipelined datapath has been adopted to boost the speed of the deblocking filter process. The processing order of the filter is rearranged to facilitate the deblocking of the pixels in a pipelined fashion. A suitable buffer mechanism has also been proposed that reduces the size of the on-chip SRAM and redundant external memory accesses. The hardware implementation, under TSMC 0.13 /spl mu/m standard cell library, consumes only 7.5 K gates at a clock frequency of 200 MHz. Our architecture supports real-time deblocking of high resolution (2048/spl times/1024) video applications at 30 fps over three channels.

The processing time needed for motion estimation usually accounts for a significant part of the overall processing time of the video encoder. To improve the video encoding speed, reducing the execution time for motion estimation process is essential. Parallel implementation of video encoding systems using either the software or the hardware approach has attracted much attention in the area of real time video coding. In this paper, we attempt to implement a video encoder on a bus network. Usually, for such a parallel system, the key concern is associated with partitioning and balancing of the computational load among the processors such that the overall processing time of the video encoder is minimized. With the use of the divisible load theory (DLT) paradigm, a strip-wise load partitioning/balancing scheme, a load distribution strategy, two implementation strategies are developed to exploit the data parallelism inherent in the video encoding process. The striking feature of our design is that,both the granularity of the load partitions and all the associated overheads caused during parallel video encoding process can be explicitly considered. This significantly contributes to the minimization of the overall processing time of the video encoder. Extensive experimental studies are carried out to test the effectiveness of the proposed strategies. The performance of the parallel video encoder is quantified using the metrics speedup and performance gain, respectively. The experimental results show that our strategies are effective for exploiting the available parallelism inherent in the video encoding process and provide a theoretical insight on how to analytically quantify and minimize the overall processing time of a parallel system. The proposed strategies can be easily extended and applied to improve other existing parallel systems.

In this paper, we propose a procedure that enhances the very low bit-rate coding performance of Set Partitioning in Hierarchical Trees (SPIHT)-based image coders. In SPIHT-based algorithms, all nodes with descendants in the LL band are initially added to the list of insignificant sets as type-A entries. Our experiments indicate that type-A nodes are highly likely to be insignificant during the sorting passes of the high bit planes. Our new procedure exploits this characteristic so that all these type-A nodes can be encapsulated into a single virtual node.

The cathode ray tube (CRT) monitor consists of alternating patterns of red, green and blue color phosphor pixels at fixed spacing. Convergence relates to a monitor's ability to match red, green and blue colors to form white. The misconvergence or misalignment of the colors results in incorrect rendition of colors, particularly at the edges. Misconvergence is an important measure of the performance of a CRT monitor. This paper presents a technique for measuring misconvergence of a color monitor by processing monochrome images of specific patterns displayed on the monitor. The technique overcomes problems due to color cross talk, video/image jitter, and lens distortion. Also, the measurement time is shorter by an order of a magnitude over traditional methods, opening up possibilities for real time feedback for accurate alignment.

This paper describes a variation of the set partitioning in hierarchical trees (SPIHT) scheme for color image coding. By using partially linked spatial orientation tree structures across different spectral planes, the new color-SPIHT scheme is able to embed both chrominance and luminance data in the coded bit stream. The performance is comparable to that of a SPIHT-based coding scheme but with significantly lower computational complexity.

In this paper, we propose a new framework to perform motion compression for time-dependent 3D geometric data. Temporal coherence in dynamic geometric models can be used to achieve significant compression, thereby leading to efficient storage and transmission of large volumes of 3D data. The displacement of the vertices in the geometric models is computed using the iterative closest point (ICP) algorithm. This forms the core of our motion prediction technique and is used to estimate the transformation between two successive 3D data sets. The motion between frames is coded in terms of a few affine parameters with some added residues. Our motion segmentation approach separates the vertices into two groups. Within the first group, motion can be encoded with a few affine parameters without the need of residues. In the second group, the vertices need further encoding of residual errors. Also in this group, for those vertices associated with large residual errors under affine mapping, we encode their motion effectively using Newtonian motion estimates. This automatic segmentation enables our algorithm to he very effective in compressing time-dependent geometric data. Dynamic range data captured from the real world, as well as complex animations created using commercial tools, can be compressed efficiently using this scheme.

The potential of the new weighted-compressive sensing approach for efficient reconstruction of electrocardiograph (ECG) signals is investigated. This is motivated by the observation that ECG signals are hugely sparse in the frequency domain and the sparsity changes slowly over time. The underlying idea of this approach is to extract an estimated probability model for the signal of interest, and then use this model to guide the reconstruction process. The authors show that the weighted-compressive sensing approach is able to achieve reconstruction performance comparable with the current state-of-the-art discrete wavelet transform-based method, but with substantially less computational cost to enable it to be considered for use in the next generation of miniaturised wearable ECG monitoring devices.

This paper describes the implementation of the recently introducedcolor set partitioning in hierarchical tree (CSPIHT)-based scheme for video coding. The intra- and interframe coding performance of a CSPIHT-based video coder (CVC) is compared against that of the H.263 at bit rates lower than 64 kbit/s. The CVC performs comparably or better than the H.263 at lower bit rates, whereas the H.263 performs better than the CVC at higher bit rates. We identify areas that hamper the performance of the CVC and propose an improved scheme that yields better performance in image and video coding in low bit-rate environments.

In this paper, we present an embedded approach for coding image regions with arbitrary shapes. Our scheme takes a different approach by separating the objects in the transform domain instead of the image domain so that only one transform for the entire image is required. We define a new shape-adaptive embedded zerotree wavelet coding (SA-EZW) technique for encoding the coefficients corresponding to specific objects in gray-scale and color-image segments by implicitly representing their shapes, thereby forgoing the need for separately coding the region boundary. At our decoder, the shape information can be recovered without separate and explicit shape coding. The implicit shape coding enables the bit stream for the object to be fully rate scalable, since no explicit bit allocation is needed for the object shape. This makes it particularly suitable when content-based functionalities are desired in situations where the user bit rate is constrained and enables precise bit-rate control while avoiding the problem of contour coding. We show that our algorithm sufficiently addresses the issue of content-based scalability and improved coding efficiency when compared with the "chroma keying" technique, an implicit shape-coding technique which is adopted by the current MPEG-4 standard.

The widespread use of block-based interframe motion estimation for video sequence compression in both MPEG and H.263 standards is due to its effectiveness and simplicity of implementation. Nevertheless, the high computational complexity of the full-search algorithm has motivated a host of suboptimal but faster search strategies. A popular example is the three-step search (TSS) algorithm. However, its uniformly spaced search pattern is not well matched to most real-world video sequences in which the motion vector distribution is nonuniformly biased toward the zero vector. Such an observation inspired the new three-step search (NTSS) which has a center-biased search pattern and supports a halfway-stop technique. It is faster on average, and gives better motion estimation as compared to the well-known TSS. Later, the four-step search (4SS) algorithm was introduced to reduce the average case from 21 to 19 search points, while maintaining a performance similar to NTSS in terms of motion compensation errors. We propose a novel unrestricted center-biased diamond search (UCBDS) algorithm which is more efficient, effective, and robust than the previous techniques. It has a best case scenario of only 13 search points and an average of 15.5 block matches. This makes UCBDS consistently faster than the other suboptimal block-matching techniques. This paper also compares the above methods in which both the processing speed and the accuracy of motion compensation are tested over a wide range of test video sequences.

© 2014 IEEE. Targeted at MSR-Bing Image Retrieval grand challenge, we accumulate the experience from the one in 2013, and the make further investigation into different models to solve the relevance assessment problem. Generally speaking, the assessment of relevance between text query and image list is very hard due to the semantic gap. It's not easy to find the 'mapping' from text query into the visual world. Solutions from 2013 MSR-Bing grand challenge are discussed in this paper. Combining with our own observation, we give some insights into why some solutions work well, while others not. Our main solution is to combine the deep learning features with the wining solution of last year (average similarity measurement and page rank), since the deep learning features have more compact representation than the traditional BoWs features, and deep learning features are efficient (on a descent GPU) with very good performance. Our solution achieved the 1st place in MSR-Bing grand challenge 2014. Finally, we give the running time of our solution in the testing phase for the 2014 ICME testing set and development set, respectively.

It has been recently shown that incorporating priori knowledge significantly improves the performance of basic compressive sensing based approaches. We have managed to successfully exploit this idea for recovering a matrix as a summation of a Low-rank and a Sparse component from compressive measurements. When applied to the problem of construction of 4D Cardiac MR image sequences in real-time from highly under-sampled $k-$space data, our proposed method achieves superior reconstruction quality compared to the other state-of-the-art methods., Comment: conference

In this paper, we propose a novel method for spatial context modeling toward boosting visual discriminating power. We are particularly interested in how to model high-order local spatial contexts instead of the intensively studied second-order spatial contexts, i.e., co-occurrence relations. Motivated by the recent success of random forest in learning discriminative visual codebook, we present a spatialized random forest (SRF) approach, which can encode an unlimited length of high-order local spatial contexts. By spatially random neighbor selection and random histogram-bin partition during the tree construction, the SRF can explore much more complicated and informative local spatial patterns in a randomized manner. Owing to the discriminative capability test for the random partition in each tree node's split process, a set of informative high-order local spatial patterns are derived, and new images are then encoded by counting the occurrences of such discriminative local spatial patterns. Extensive comparison experiments on face recognition and object/scene classification clearly demonstrate the superiority of the proposed spatial context modeling method over other state-of-the-art approaches for this purpose.

Attention is an integral part of the human visual system and has been widely studied in the visual attention literature. The human eyes fixate at important locations in the scene, and every fixation point lies inside a particular region of arbitrary shape and size, which can either be an entire object or a part of it. Using that fixation point as an identification marker on the object, we propose a method to segment the object of interest by finding the “optimal” closed contour around the fixation point in the polar space, avoiding the perennial problem of scale in the Cartesian space. The proposed segmentation process is carried out in two separate steps: First, all visual cues are combined to generate the probabilistic boundary edge map of the scene; second, in this edge map, the “optimal” closed contour around a given fixation point is found. Having two separate steps also makes it possible to establish a simple feedback between the mid-level cue (regions) and the low-level visual cues (edges). In fact, we propose a segmentation refinement process based on such a feedback process. Finally, our experiments show the promise of the proposed method as an automatic segmentation framework for a general purpose visual system.

In this paper, we propose a data-driven approach that extracts prior information for segmentation of the left ventricle in cardiac MR images of transplanted rat hearts. In our approach, probabilistic priors are generated from prominent features, i.e., corner points and scale-invariant edges, for both endo- and epi-cardium segmentation. We adopt a level set formulation that integrates probabilistic priors with intensity, texture, and edge information for segmentation. Our experimental results show that with minimal user input, representative priors are correctly extracted from the data itself, and the proposed method is effective and robust for segmentation of the left ventricle myocardium even in images with very low contrast. More importantly, it avoids inter- and intra- observer variations and makes accurate quantitative analysis of low-quality cardiac MR images possible.

1. Prior extraction We first generate priors for the LV endocardium and epicardium respectively by extracting corner points inside the LV cavity (Fig. 1b) and the scale-invariant edges (Fig. 1d). For endocardium, the probabilistic map is obtained by kernel density estimation (Fig. 1c); for epicardium, the probabilistic map is obtained by measuring the likelihood of the extracted edges being on the true epicardial boundary (Fig. 1f). 2. Temporal correlation Instead of segmenting each image independently, we incorporate the temporal correlation among frames. Given one reference frame with reliable segmentation, the myocardial contours in the reference frame are propagated to other frames in the same sequence by non-rigid registration. Based on registration error, we then generate a confidence map for each frame, indicating the reliability of the propagated contours.

This paper presents a novel approach for image segmentation by introducing competition between neighboring shape models. Our method is motivated by the observation that evolving neighboring contours should avoid overlapping with each other and this should be able to aid in multiple neighboring objects segmentation. A novel energy functional is proposed, which incorporates both prior shape information and interactions between deformable models. Accordingly, we also propose an extended maximum a posteriori (MAP) shape estimation model to obtain the shape estimate of the organ. The contours evolve under the influence of image information, their own shape priors and neighboring MAP shape estimations using level set methods to recover organ shapes. Promising results and comparisons from experiments on both synthetic data and medical imagery demonstrate the potential of our approach.

Precise segmentation of three-dimensional (3D) magnetic resonance angiography (MRA) image can be a very useful computer aided diagnosis (CAD) tool in clinical routines. Our objective is to develop a specific segmentation scheme for accurately extracting vasculature from MRA images. Our proposed algorithm, called the capillary active contour (CAC), models capillary action where liquid can climb along the boundaries of thin tubes. The CAC, which is implemented based on level sets, is able to segment thin vessels and has been applied for verification on synthetic volumetric images and real 3D MRA images. Compared with other state-of-the-art MRA segmentation algorithms, our experiments show that the introduced capillary force can facilitate more accurate segmentation of blood vessels.

A method based upon subjective viewing tests to evaluate the perceptual impact of different extents of edge sharpness is presented and the most eye-pleasing sharpness (MEPS) for an image edge-sharpening process is derived. The findings with Laplacian of Gaussian edge-enhancement filter show that the baseline MEPS is about 2.6 times that of the local just-noticeable difference, and the actual MEPS is also dependent on the contrast increase in the surrounding areas. The proposed methodology can be used to determine the MEPS for a particular edge-enhancement process, and the resultant formulation for the perceptual impact of edge sharpness can be used for reference in control of edge enhancement, image reconstruction and de-blurring processing, as well as objective visual quality gauge.