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Quality of food and agricultural products is vital for farmers and consumers. Quality based classification of these products is being carried out manually in the industry which is tedious and expensive. Computer Vision systems can be used to automate the classification process. Automation can reduce the production cost and improve the overall quality. A computer vision system captures the image of...
This paper proposes a novel model for contrast enhancement of RGB images. The average local contrast measure is increased within a variational framework which preserves the hue of the original image by coupling the channels. The user is enabled to intuitively control the level of the contrast as well as the scale of the enhanced details. Moreover, our model avoids large modifications of the original...
In this paper, we propose a new Multi-kernel Metric Learning (MKML) approach to enhance the performance of person re-identification using adaptive weighted Multi-kernel. The intuition behind our approach is that different features, i.e., low-level and middle-level features, have different nature and thus discriminating capability, utilizing different kernels could map these features into sub-spaces,...
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...
We proposed a novel model to predict human's visual attention when free-viewing webpages. Compared with natural images, webpages are usually full of salient regions such as logos, text, and faces, while few of them attract human's attention in a short sight. Moreover, webpages perform distinct viewing patterns which are quite different from the natural images. In this paper, we introduced multi-features...
In this paper we introduce a novel representation for the classification of 3D images. Unlike most current approaches, our representation is not based on a fixed pyramid but adapts to image content and uses image regions instead of rectangular pyramid scales. Image characteristics, such as depth and color, are used for defining regions within images. Multiple region scales are formed in order to construct...
Bilateral filtering is a commonly used technique in image processing. However, being nonlinear, it is computationally expensive. The situation gets worse while the filter radius grows up. Several works have been proposed to accelerate the computation. Nevertheless, most techniques are tailored for grayscale image bilateral filtering or confined to specific kernel functions. In this paper, we propose...
In this paper, we consider a natural extension of the edge-preserving bilateral filter for vector-valued images. The direct computation of this non-linear filter is slow in practice. We demonstrate how a fast algorithm can be obtained by first approximating the Gaussian kernel of the bilateral filter using raised-cosines, and then using Monte Carlo sampling. We present simulation results on color...
Retinal image quality assessment (IQA) algorithms use different hand crafted features for training classifiers without considering the working of the human visual system (HVS) which plays an important role in IQA. We propose a convolutional neural network (CNN) based approach that determines image quality using the underlying principles behind the working of the HVS. CNNs provide a principled approach...
We propose an approach to indoor occupant localization using a network of single-pixel, visible-light sensors. In addition to preserving privacy, our approach vastly reduces data transmission rate and is agnostic to eavesdropping. We develop two purely data-driven localization algorithms and study their performance using a network of 6 such sensors. In one algorithm, we divide the monitored floor...
Nowadays, a consequence of data overload is that world's technology capacity to collect, communicate, and store large volumes of data is increasing faster than human analysis skills. Such an issue has motivated the development of graphic ways to visually represent and analyze high-dimensional data. Particularly, in this work, we propose a graphical interface that allow the combination of dimensionality...
Many features have been proposed for person re-identification, but most of them are the combination of several different kinds of single features. And there is no research about what role the single features play and which can be fused together to improve the performance. In this paper, we explore eight single features (four colors, two textures, two gradients) and their fusions. Evaluations are conducted...
Unmanned aerial vehicles (UAV), also referred to as drones, are a growing field in computer science with applications in military systems, delivery services, emergency relief and evacuation. One of the primary obstructions to the allowance of UAV journeys over populated areas is the lack of sophisticated automated systems that detect drone landing sites. In this paper, we propose a landing area detection...
With the development of stone processing and sales, effective stone surface texture image recognition methods are needed. We proposed a new stone surface texture image recognition method based on texture and colour. We combine the following visual features: Gabor features which can well simulate the single cell sensing profile of mammalian visual neurons, The Grey-level Co-occurrence Matrices(GLCM)...
Diagnosis of rice planthopper pests based on imaging technology is an efficient means to develop intelligent agriculture. Effective contour automation extraction is an important pretreatment technology at the early stage for identifying and classifying rice planthoppers. For the curtain as the background contained texture structures and resulted in a heterogeneous texture in the sensed image, which...
Separating the foreground objects from the complex background in a static image is one of the research hot spots in computer vision. Due to lack of motion information, most of the current approaches only explore local object cues in the segment-level which easily suffer from not only the view and illumination changes, but also deformation and occlusion. This paper proposed a new multi-class object...
Application of the benefits of modern computing technology to improve the efficiency of agricultural fields is inevitable with growing concerns about increasing world population and limited food resources. Computing technology is crucial not only to industries related to food production but also to environmentalists and other related authorities. It is expected to increase the productivity, contribute...
Correlation filter-based trackers achieve very good performance in visual tracking, but the traditional correlation tracking methods failed in mining the color information of the image sequence. To solve this problem, we propose a novel and robust scale adaptive tracker combined with color attributes in correlation filter framework, which extracts not only gray but also color information as the feature...
In this paper, we propose combined visual features for person re-identification. Our features are based on the multiple hand-crafted visual features. The proposed features are a combination of histogram from the RGB, YUV and HSV color channels, LBP and SIFT features. Then we use different distance metric learning methods to measure the similarity of the same persons and different persons. Experimental...
This paper focuses on the clustering segmentation of 3D color point cloud. We extend the mean shift algorithm to the 3D xyz space, and what's more, we also consider the rgb color information, so the 6 dimensional data is adopted in the algorithm. The cluster center converges to the joint position of the local maximum density and the minimum gradient change of color, so our clustering segmentation...
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