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In this paper, we address the problem of detecting and segmenting partial image blur from a single input image. Instead of assuming particular image priors or requiring additional user annotation, we propose a novel learning framework which jointly solves the tasks of blur kernel estimation and image blur segmentation, so that partial image blur can be automatically separated from the remaining parts...
In this paper, we present a novel denoising algorithm based on the Rodin-Osher-Fatemi (ROF) model. The goal is to ensure maximum noise removal while preserving image details. To achieve this goal, we developed a new edge detector based on the structure tensor, Non-Local Mean filtering and fuzzy complement. This edge detector is incorporated in the objective function of the ROF model to introduce more...
Soft computing in the field of agriculture science is being employed with computer vision techniques in order to detect the diseases in crops to increase the overall yield. A Modified Rotation Kernel Transformation(MRKT) based directional feature extraction scheme is presents to resolve the issues occurring due to shape, color or other deceptive features during plant disease recognition. The MRKT...
A critical task in corner detection in 2D images is on the distinction between a corner pixel and a pixel with a large gradient (i.e., an edge pixel). Imbalanced point detection was proposed to address this problem, where a corner pixel is characterized as a pixel with an imbalanced appearance, while an edge pixel has the opposite property. With extensive experiments, an imbalanced point detector...
Anomaly detection in hyperspectral images aims at detecting small size objects of unknown spectra. The major problem with anomaly detection is the absence of prior knowledge. Consequently, the extraction of true anomalies from the background and noise is a challenging task. In fact, the image scene already contains the background, noises and anomalous pixels and even in presence of prior knowledge,...
In this work, two enhancement methods are proposed to speed up junction detection performed by the JUDOCA detector. The first enhancement method minimizes the number of junction candidates on which the circular kernel is applied. This is achieved by introducing a suppression technique that takes both the thin and thick edge images into consideration. The second method works on relaxing the step of...
Objective of the study is to suggest a hybrid computational method to enhance text from historical inscriptional images by differentiating foreground and background of the inscriptional images using hysteresis thresholding based on various edge detectors such as canny, sobel and laplacian techniques. Furthermore, the combined thresholding methods based on edge detectors were estimated to determine...
This paper presents a fast deblurring algorithm to remove camera motion blur from a single photograph using built-in gyroscopes and strong edge prediction. An inaccurate blur kernel or point spread function (PSF) usually leads to an unsatisfying restored result. Hence, we propose a robust three-phase method for accurate PSF estimation. In the first stage, we utilize the embedded gyroscopes to compute...
Text data present in scene images may be the important clue for indexing, automatic footnote, and indexing of images. Now-a-days extraction of text from images has become one of the fastest growing research areas in the field of computer vision. In scene images, text data are present with huge variations in font sizes, styles, alignments, and orientations. These variations make the task of detection...
License Plate Recognition System (LPRS) plays a vital role in smart city initiatives such as traffic control, smart parking, toll management and security. In this article, a cloud-based LPRS is addressed in the context of efficiency where accuracy and speed of processing plays a critical role towards its success. Signature-based features technique as a deep convolutional neural network in a cloud...
Passive millimeter-wave images (PMMW) often suffer from issues such as low resolution, noise, and blurring. In this paper, we proposed a blind image deconvolution method for the passive millimeter-wave images. The purpose of the proposed method is to simultaneously solve the point spread function (PSF) and restoration image. In this method, the data fidelity item is constructed based on Gaussian noise...
This paper presents an acceleration method of the bilateral filter (BF) for multi-channel images. In most existing acceleration methods, the BF is approximated by an appropriate combination of convolutions. A major purpose under this framework is to achieve sufficient approximate accuracy by as few convolutions as possible. However, state-of-the-art methods for multi-channel images still requires...
We propose a simple yet effective blur kernel re-initialization method in a coarse-to-fine framework for blind image deblurring. The proposed method is motivated by observing that most deblurring algorithms use only an estimated blur kernel at the coarser level to initialize a blur kernel for the next finer level. Based on this observation, we design an objective function to exploit both a blur kernel...
Stereo matching methods estimate depth information of captured images. One way to estimate accurate depth values is to use the distance information. This method enhances the disparity map by preserving the edge region. In order to preserve the depth discontinuity near the edge region, it uses the distance information as a new weighting value for the matching cost function. However, this method has...
Disentangling multicomponent nonstationary signals into coherent AM-FM modes is usually achieved by identifying “loud” time-frequency trajectories where energy is locally maximum. We will present here an alternative perspective that is based on “silent” points, namely spectrogram zeros. The rationale and the implementation of the approach will be discussed, as well as an application to the characterization...
Dermoscopy images usually suffer from spatially-varying defocus blur, which will easily influence the lesion analysis result and lead to wrong aided diagnosis. In this paper, a novel blind deblurring framework is proposed for dermoscopy images with spatially-varying defocus blur. The defocus map is firstly estimated by support vector regressor (SVR) learning model using the natural scene statistics...
A new edge-directional image resampling method is proposed. The method uses a weighted sum of two adaptive 4x4 interpolation kernels to construct high-resolution pixels. The weights are chosen according to the local gradient features for each pixel. The interpolation kernels are learned using pairs of low- and high-resolution images taken from LIVE image database. The method has low complexity and...
In this paper, an object detection and localization method for bin picking of plastic wrapped objects is described. Since such objects are deformable and have non-Lambertian surfaces, it is difficult to apply conventional feature point approaches or edge based template matching. To solve this problem, we propose a new method which is called “KCS (kernel convolution score)”. It measures the total score...
We analyze and propose an improved implementation of joint bilateral upsampling algorithm [5] for depth image super-resolution (SR). The input to the algorithm is a low resolution (LR) depth image and its corresponding high resolution (HR) color image. With the guidance of HR color image, the depth edges can be preserved during the SR process. However, in the original implementation, the sparse sampling...
Robust estimation of linear structures such as edges and junction features in digital images is an important problem. In this paper, we use an adaptive robust structure tensor (ARST) method where the local adaptation process uses spatially varying adaptive Gaussian kernel that is initialized using the total least-squares structure tensor solution. An iterative scheme is designed with size, orientation,...
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