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In this paper, we study the problem of fine-grained image categorization, which is much more useful in real applications than basic image classification. Based on the most challenge dataset, CUB-200, we combine Efficient match kernel (EMK) with the weighted spatial pyramid to achieve state-of-art performance. Comparison with BoW, which can also be viewed as kernel matching approach, EMK digs the relations...
Recognition of video scenes is a challenging problem due to the unconstrained structure of the video content. Here, we propose a spatial pyramid based method for the recognition of video scenes and explore the effect of parameter optimization to the recognition accuracy. In the experiments different sampling methods, dictionary sizes, kernel methods, and pyramid levels are examined. Support Vector...
Carrying out effective and sustainable agriculture product has become an important issue in recent years. Agricultural production has to keep up with an ever-increasing population. A key to this is the usage of modern techniques (for precision agriculture) to take advantage of the quality in the market. The paper reviews various quality evaluation and grading techniques of Oryza Sativa L. (rice) in...
Deep learning technology and related algorithms have dramatically broken landmark records for a broad range of learning problems in vision, speech, audio, and text processing. Meanwhile, kernel methods have found common-place usage due to their nonlinear expressive power and elegant optimization formulation. Based on recent progress in learning high-level, class-specific features in unlabeled data,...
The localization of anomalous activity in graphs is a statistical problem that arises in many applications, such as network surveillance, disease outbreak detection, and activity monitoring in social networks. We will address the localization of a cluster of activity in Gaussian noise in directed, weighted graphs. We develop a penalized likelihood estimator (we call the relaxed graph scan) as a relaxation...
Crowd density estimation is the fundamental content and central issue in most public video monitoring systems, and it is also a hot spot in computer vision area. Recent state-of-the-art method is based on image processing. Traditional methods can be divided into two main directions, one is based on pixel statistics, and the other is based on texture analysis. In our paper, we combine these two methods...
Automatic vehicle Make and Model Recognition (MMR) system offers a competent way to vehicle classification and recognition systems. This paper proposes a real time while robust vehicle make and model recognition system extracting the vehicle sub-image from the background and studies some sparse feature coding methods such as Orthogonal Matching Pursuit (OMP), some variation of Sparse Coding (SC) methods...
In this paper, we propose the application of principal component analysis (PCA) to scale-spaces. PCA is a standard method used in computer vision tasks such as recognition of eigenfaces. Because the translation of an input image into scale-space is a continuous operation, it requires the extension of conventional finite matrix based PCA to an infinite number of dimensions. Here, we use spectral theory...
In last decade lot of efforts had been made by research community to create sign language recognition system which provide a medium of communication for differently-abled people and their machine translations help others having trouble in understanding such sign languages. Computer vision and machine learning can be collectively applied to create such systems. In this paper, we present a sign language...
The identification of shadow and shading boundaries is a key step towards reducing the imaging effects that are caused by direct illumination of the light source in the scene. Discriminating shadow boundaries from images of natural scenes has been widely applied in the field of computer vision such as object recognition, intelligent monitoring and image understanding. In this paper, we propose a method...
Single image dehazing has been a challenging problem due to its ill-posed nature. Most of the existing dehazing algorithms are based on single atmospheric scattering model. However, this model is not capable of explaining complex effects of weather on images as multiple scattering occurs in real world. Existing multiple scattering models are widely utilized for simulating various weather condition;...
In this paper we present an integral image algorithm that can run in real-time on a Graphics Processing Unit (GPU). Our system exploits the parallelisms in computation via the NVIDA CUDA programming model, which is a software platform for solving non-graphics problems in a massively parallel high performance fashion. We compare the performance of the parallel approach running on the GPU with the sequential...
Real-world scenes involve many objects that interact with each other in complex semantic patterns. For example, a bar scene can be naturally described as having a variable number of chairs of similar size, close to each other and aligned horizontally. This high-level interpretation of a scene relies on semantically meaningful entities and is most generally described using relational representations...
This paper proposes a method for single image enlargement with linear weighting techniques and kernel estimation. The aims of our technique are to reduce the distance of the pixel value too far especially for interpolation pixel. Contribution from the closest pixels to the interpolation point is unchanged meanwhile the farthest pixel contribution will be estimated. There are four pixel contributions...
The extraction of nuclei from Haematoxylin and Eosin (H&E) stained biopsies present a particularly steep challenge in part due to the irregularity of the high-grade (most malignant) tumors. To your best knowledge, although some existing solutions perform adequately with relatively predictable low-grade cancers, solutions for the problematic high-grade cancers have yet to be proposed. In this...
Category 4. A machine learning based methodology is proposed to recognize a predefined set of hand gestures using depth images. For such purpose, a RGBD sensor (Microsoft kinect) is employed to track the hand position. Thus, a preprocessing stage is presented to subtract the region of interest from depth images. Moreover, a learning algorithm based on kernel methods is used to discover the relationships...
Before matching the video frames in Scale-Invariant Feature Transform (SIFT) algorithm, the key-points must be extracted firstly. If the size and characteristic of input images are changed, gray threshold of key-points must be reinstalled, to avoid extremely computation cost or failure in registration. In this paper, a self-adaptive SIFT key-points extraction algorithm for video images is developed...
In the field of computer vision, image matching is very important in many applications, such as the object recognition, 3D reconstruction, stereo vision, motion tracking and augmented reality. A method of improving the Opensurf algorithm used in AR for decreasing matching points and mismatch and increasing the calculation speed is proposed.
We present a novel approach to improving subspace clustering by exploiting the spatial constraints. The new method encourages the sparse solution to be consistent with the spatial geometry of the tracked points, by embedding weights into the sparse formulation. By doing so, we are able to correct sparse representations in a principled manner without introducing much additional computational cost....
This paper addresses the problem of image alignment using direct intensity-based methods for affine and homography transformations. Direct methods often employ scale-space smoothing (Gaussian blur) of the images to avoid local minima. Although, it is known that the isotropic blur used is not optimal for some motion models, the correct blur kernels have not been rigorously derived for motion models...
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