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Shadow removal is a challenging task as it requires the detection/annotation of shadows as well as semantic understanding of the scene. In this paper, we propose an automatic and end-to-end deep neural network (DeshadowNet) to tackle these problems in a unified manner. DeshadowNet is designed with a multi-context architecture, where the output shadow matte is predicted by embedding information from...
this work considers the algorithm of mobile robot recognition and localization on the basis of color patterns, applied in robosoccer. For exact position definition of mobile robots and a ball in robosoccer it is necessary to analyze the image received from camera. Whereas each of these objects on the image has the color pattern consisting of circles, the first step of algorithm is detecting of circles...
Underwater images are known to be strongly deteriorated by a combination of wavelength-dependent light attenuation and scattering. This results in complex color casts that depend both on the scene depth map and on the light spectrum. Color transfer, which is a technique of choice to counterbalance color casts, assumes stationary casts, defined by global parameters, and is therefore not directly applicable...
Single materials have colors which form straight lines in RGB space. However, in severe shadow cases, those lines do not intersect the origin, which is inconsistent with the description of most literature. This paper is concerned with the detection and correction of the offset between the intersection and origin. First, we analyze the reason for forming that offset via an optical imaging model. Second,...
Shadows have a significant effect on the performance of many computer vision tasks, such as object tracking, action recognition, and structure health monitoring. In many object detection systems, shadows are often misclassified as parts of the moving objects or independent moving objects. As a result, the performance of these subsequent higher-level tasks is adversely affected. This paper presents...
In this paper, we propose a computer vision system that detects the external defects of orange citrus fruits using multi-spectral imaging sensor. First, the proposed algorithm segments the orange fruit from the captured Near-Infra Red (NIR) and RGB images using only the NIR component. Second, some adaptive pre-processing techniques are applied on the segmented RGB and NIR orange fruit images. Hence,...
Color constancy is the ability of the human visual system to perceive constant colors for a surface despite changes in the spectrum of the illumination. In computer vision, the main approach consists in estimating the illuminant color and then to remove its impact on the color of the objects. Many image processing algorithms have been proposed to tackle this problem automatically. However, most of...
Specular reflection removal is indispensable to many computer vision tasks. However, most existing methods fail or degrade in complex real scenarios for their individual drawbacks. Benefiting from the light field imaging technology, this paper proposes a novel and accurate approach to remove specularity and improve image quality. We first capture images with specularity by the light field camera (Lytro...
Lane detection is one of the most challenging problems in machine vision and still has not been fully accomplished because of the highly sensitive nature of computer vision methods. Computer vision depends on various ambient factors. External illumination conditions, camera and captured image quality etc. effect machine vision performance. Lane detection faces all these challenges as well as those...
Quality checking is a major aspect in production. Fatigue and boredom are major parameters of human involved operating which causes for efficiency and productivity. Therefore Biscuit manufacturing industries have to overcome these situations throughout finding novel ways and methods for their human involved quality inspection systems. This paper proposes a computer vision based quality checking system...
Robust object tracking is a challenging task in computer vision. Color features have been popularly used in visual tracking. However, most conventional color-based trackers either rely on luminance information or use simple color representations for image description. During the tracking sequences, the perceived color of the target may change because of the varying lighting conditions. In this paper,...
The suitability of the Multimodal Neighbourhood Signature (MNS) method for illumination invariant recognition is investigated. The MNS algorithm directly formulates the problem of extracting illumination invariants from local colour appearance of an object. The invariants are the channel-wise ratio and the cross-ratio computed from modes (pairs of modes respectively) of colour density function in...
It is extremely time consuming for researchers looking for particular events of interest to manually search in the video database. Therefore, there is enormous scope in research in the field of automatic extraction of key frames from underwater video sequences. Analysis of underwater video poses many challenges to existing techniques in computer vision including camera movement, turbidity, uneven...
Nowadays, automatic reading of passport information has become an important security issue in airports and border control. This is implemented including electronic devices on passports that allow a reliable and fast reading. Our purpose with this system is to reinforce the electronic reading adding an optical reading system. This optical system could be used to read old passports that do not have...
The color perception of a surface as invariant under changing illumination, called color constancy, is very important in applications such as outdoor navigation. Most color constancy approaches are targeted for single images obtained from cameras whose sensors are assumed to be narrow-band. Algorithms that take advantage of the related information in consecutive frames are rare and require the presence...
This paper describes the initial design of a computer vision application to recognize regulatory traffic signs vertically installed on Colombian roads using machine learning. This application is conceived as a module of a driver assistance system under development, and an autonomous vehicle adapted to the local infrastructure. The application was trained and tested with official synthetic images provided...
A new color attention preserved sparse generative object model is proposed to handle occlusion and illumination variations in the visual tracking task. The color attention is represented by the fast calculated color descriptor on color names, which is used to weight the similarity measurement of the sparse generative model. In the sparse generative model, the image region of the object is divided...
In this paper, we present an automatic method to remove shadows in light field images. Taking into account the internal structure of the light field data, depth map of the captured scene is extracted to calculate the surface normal. Using nonlocal matching by combining chromaticity, normal and spatial location information in an anisotropic window, the shadow confidence of each pixel is established...
Traffic light is one of the important signs for drivers that help managing the car flow and reducing accident on the road. As of today technology, there exists a traffic light detection system that warns the driver to reduce the accident significantly. In this paper, we are concerned with only the red and yellow traffic light to reduce false positive and time consumption. The fast radial symmetry...
We present an approach which enables real-time augmentation of an environment composed of materials with different texture and reflectance properties without the need of application-specific hardware or extensive preparation. Our solution uses a set of RGB images of a reconstructed model to optimize the reflectance parameters and light location. Each image is decomposed into its specular and diffuse...
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