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An efficient stereo matching algorithm, which applies adaptive smoothness constraints using texture and edge information, is proposed in this work. First, we determine non-textured regions, on which an input image yields flat pixel values. In the non-textured regions, we penalize depth discontinuity and complement the primary CNN-based matching cost with a color-based cost. Second, by combining two...
Comparing to the low level local features, Transform Invariant Low-Rank Textures (TILT) can in some sense globally rectify a large class of low-rank textures in 2D images, and thus more accurate and robust. However, TILT is still rather rudimentary, and have some limitations in applications. In this paper, we proposed a novel algorithm for better solving TILT. Our method is based on the application...
Sequential dictionary learning via the K-SVD algorithm has been revealed as a successful alternative to conventional data driven methods such as independent component analysis (ICA) for functional magnetic resonance imaging (fMRI) data analysis. fMRI data sets are however structured data matrices with notions of spatio-temporal correlation. This prior information has not been included in the K-SVD...
The demands for high quality multimedia contents and the advent of the Ultra High Definition (UHD) resolution have motivated the development of the High Efficiency Video Coding (HEVC) standard, which outperforms prior standards by up to 50% in terms of coding efficiency. This improvement, however, involves higher computational complexity in the encoder side, making it essential for realtime encoders...
We address the problem of how to design a more effective co-training scheme to tackle the multi-view spectral clustering. The conventional co-training procedure treats information from all views equally and often converges to a compromised consensus view that does not fully utilize the multiview information. We instead propose to learn an augmented view and construct its corresponding affinity matrix...
In the last years, the increasing availability of annotated data has facilitated the great success of supervised learning in real-world applications such as semantic labeling. However, the vast majority of data is nowadays unlabeled or partially annotated. In this paper, we develop an Expected Marginal Latent Structural SVM (EM-LSSVM) framework for performing structured learning in the presence of...
Markov Random Field (MRF) algorithms are powerful tools in image analysis to explore contextual information of data. However, the application of these methods to large data means that alternative approaches must be found to circumvent the NP-hard complexity of the MRF optimization. We introduce a MRF-based framework that overcomes this issue by using graph partitioning. The computational complexity...
Non-Bayer color filter array (CFA) sensors have recently drawn attention due to their superior compression of spectral energy, ability to deliver improved signal-to-noise ratio, or ability to provide high dynamic range (HDR) imaging. Demosaicking methods that perform color interpolation of Bayer CFA data have been widely investigated. However, a bottleneck to the adaption of emerging non-Bayer CFA...
Binary embedding of high-dimensional data aims to produce low-dimensional binary codes while preserving discriminative power. State-of-the-art methods often suffer from high computation and storage costs. We present a simple and fast embedding scheme by first downsampling N-dimensional data into M-dimensional data and then multiplying the data with an M×M circulant matrix. Our method requires O(N...
We propose a novel computationally efficient hierarchical dictionary learning (HDL) approach for data-driven unmixing and functional connectivity analysis of functional magnetic resonance imaging (fMRI) data. It is shown that by simultaneously exploiting the sparsity of the spatial brain maps and the incoherence among their evolution in time or task functions, one can achieve better performance while...
This work presents an intelligent analog image sensor system for smart camera applications with the need of edge or marker detection. The system consists of a 3×3 read-out CMOS image sensor, an analog Sobel stage and additional circuitry like operational amplifiers and comparators to compute a 1 bit image with the edges present in the taken photo. This information can then be further processed digitally...
Directly connected to the texture appearance, texture granularity is an effective measurement for geographic resources classification, product quality monitoring and image compression ratio selection. However, the application of existing works on texture granularity is limited by intense computation and the dependence on empirically selected parameters that vary among different textures. This paper...
The latest high efficiency video coding (HEVC) standard achieves about 50% bit-rate reduction at equivalent visual quality compared to H.264/AVC. Sample adaptive offset (SAO) is one of the newly adopted tools right after deblocking filter, which can improve both coding efficiency and visual quality. However, for real-time encoding scenarios, the complexity of SAO is usually too high to be enabled...
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We present a refinement framework for background subtraction based on color and depth data. The foreground objects are segmented based on color and depth data independently, in which all of the existed background subtraction (BGS) methods can be applied. The two detected foregrounds will be very inaccurate in some situations such as shadowing and color camouflage. We focus our works on refining the...
In this paper we address the problem of learning image structures directly from sparse codes. We first model images as linear combinations of molecules, which are themselves groups of atoms from a redundant dictionary. We then formulate a new structure learning problem that learns molecules directly from image sparse codes, namely from the image representation in the atom domain. We build on a structural...
This paper introduces row-column transforms (RCTs) which are 2D non-separable transforms defined with the aid of a set of 1-D linear transforms and a basis ordering permutation. We propose a novel method for the design of row-column transforms that approximate desired complex transforms (such as KLTs, SOTs, etc.) so that most of the performance of the approximated transforms is retained at significantly...
This paper presents a novel approach to detecting crowd groups and learning semantic regions with a Gestalt laws-based similarity. Different from the existing approaches based on optical flows or complete trajectories, our model adopts tracklets as the original input, because they carry more detailed information. Though those tracklets do not appear in the same duration, they are more robust to noise...
Texture is considered one of most significant information sources in histo-pathology image analysis. To take advantage of information on color texture in digital histo-pathology, this work analyzes inherent characteristics of the hue component in the cylindrical color space, and introduces an effective color texture descriptor based on the LBP paradigm. Unlike existing LBP variants designed for linear...
This paper proposes a novel method for designing compactly supported biorthogonal graph wavelet filter banks with flat spectral responses. We firstly construct a class of biorthogonal graph filter banks by using the polynomial half-band kernels, and then present a design method for the polynomial half-band kernel. The proposed design method utilizes the PBP (Parametric Bernstein Polynomial), which...
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