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PDF | The computational analysis of images is challenging as it usually involves In this paper, the methods that we have developed for processing and analyzing 15+ million members; + million publications; k+ research projects. 𝗣𝗗𝗙 | Digital Image Processing (DIP) is the process of digital images All research papers submitted to the journal will be double - blind peer. Image processing, Field Programmable Gate Array (FPGA), Application Specific cryptography domain has been taking a larger space in current research era.
This book covers the fundamental basis of the optical and image processing techniques by integrating contributions from both optical and digital research communities to solve current application bottlenecks, and give rise to new applications and solutions.
Besides focusing on joint research, it also aims at disseminating the knowledge existing in both domains.
Applications covered include image restoration, medical imaging, surveillance, holography, etc It could be used as an adjunct to a standard textbook, or as a source for further reading and research project ideas. Overall, this is a very good book that deserves to be on the bookshelf of a serious student or scientist working in these areas. Politecnica de Madrid Spain in Thereafter, he obtained the PhD degree in Telecommunication Engineering at the same University in Traditional image quality assessment IQA methods do not perform robustly due to the shallow hand-designed features.
It has been demonstrated that deep neural network can learn more effective features than ever. In this paper, we describe a new deep neural network to predict the image quality accurately without relying on the reference image. To learn more effective feature representations for no Salient segmentation aims to segment out attention-grabbing regions, a critical yet challenging task and the foundation of many high-level computer vision applications.
It requires semantic-aware grouping of pixels into salient regions and benefits from the utilization of global multi-scale contexts to achieve good local reasoning. Previous works often address it as two-class segmentation problems Dilated convolutions support expanding receptive field without parameter exploration or resolution loss, which turn out to be suitable for pixel-level prediction problems.
In this paper, we propose multiscale single image super-resolution SR based on dilated convolutions. We adopt dilated convolutions to expand the receptive field size without incurring additional computational complexity.
We mi In this paper, we propose a novel deep generative approach to cross-modal retrieval to learn hash functions in the absence of paired training samples through the cycle consistency loss. Our proposed approach employs adversarial training scheme to learn a couple of hash functions enabling translation between modalities while assuming the underlying semantic relationship.
To induce the hash codes wi Image quality assessment IQA aims to use computational models to measure the image quality consistently with subjective evaluations.
The well-known structural similarity index brings IQA from pixel- to structure-based stage. Detecting regions of change in multiple images of the same scene taken at different times is of widespread interest due to a large number of applications in diverse disciplines, including remote sensing, surveillance, medical diagnosis and treatment, civil infrastructure, and underwater sensing. This paper presents a systematic survey of the common processing steps and core decision rules in moder Recent studies have shown the effectiveness of using depth information in salient object detection.
However, the most commonly seen images so far are still RGB images that do not contain the depth data.
Meanwhile, the human brain can extract the geometric model of a scene from an RGB-only image and hence provides a 3D perception of the scene. Inspired by this observation, we propose a new concept Without any prior structure information, nuclear norm minimization NNM , a convex relaxation for rank minimization RM , is a widespread tool for matrix completion and relevant low-rank approximation problems.
Nevertheless, the result derivated by NNM generally deviates the solution we desired, because NNM ignores the difference between different singular values. In this paper, we present a non-c This paper presents a technique for motion detection that incorporates several innovative mechanisms.
For example, our proposed technique stores, for each pixel, a set of values taken in the past at the same location or in the neighborhood. It then compares this set to the current pixel value in order to determine whether that pixel belongs to the background, and adapts the model by choosing rando Light field LF photography is an emerging paradigm for capturing more immersive representations of the real world.
However, arising from the inherent tradeoff between the angular and spatial dimensions, the spatial resolution of LF images captured by commercial micro-lens-based LF cameras is significantly constrained. In this paper, we propose effective and efficient end-to-end convolutional neu We propose a new model for active contours to detect objects in a given image, based on techniques of curve evolution, Mumford-Shah functional for segmentation and level sets.
Our model can detect objects whose boundaries are not necessarily defined by the gradient. We minimize an energy which can be seen as a particular case of the minimal partition problem.
In the level set formulation, t Conventional haze-removal methods are designed to adjust the contrast and saturation, and in so doing enhance the quality of the reconstructed image. Unfortunately, the removal of haze in this manner can shift the luminance away from its ideal value. In other words, haze removal involves a tradeoff between luminance and contrast.
We reformulated the problem of haze removal as a luminance reconstru Data-driven saliency has recently gained a lot of attention thanks to the use of convolutional neural networks for predicting gaze fixations.
In this paper, we go beyond standard approaches to saliency prediction, in which gaze maps are computed with a feed-forward network, and present a novel model which can predict accurate saliency maps by incorporating neural attentive mechanisms. The core of Nonlocal texture similarity and local intensity smoothness are both essential for solving most image inpainting problems. In this paper, we propose a novel image inpainting algorithm that is capable of reproducing the underlying textural details using a nonlocal texture measure and also smoothing pixel intensity seamlessly in order to achieve natural-looking inpainted images.
For matching texture, One key challenging issue of facial expression recognition is to capture the dynamic variation of facial physical structure from videos. In this paper, we propose a part-based hierarchical bidirectional recurrent neural network PHRNN to analyze the facial expression information of temporal sequences. Recently, a great progress in automatic image captioning has been achieved by using semantic concepts detected from the image. However, we argue that existing concepts-to-caption framework, in which the concept detector is trained using the image-caption pairs to minimize the vocabulary discrepancy, suffers from the deficiency of insufficient concepts.
The reasons are two-fold: Depth image super-resolution is a significant yet challenging task. In this paper, we introduce a novel deep color guided coarse-to-fine convolutional neural network CNN framework to address this problem.
First, we present a data-driven filter method to approximate the ideal filter for depth image super-resolution instead of hand-designed filters. Based on large data samples, the filter learned The IEEE Transactions on Image Processing covers novel theory, algorithms, and architectures for the formation, capture, processing, communication, analysis, and display of images, video, and multidimensional signals in a wide variety of applications.
Topics of interest include, but are not limited to, the mathematical, statistical, and perceptual modeling, representation, formation, coding, filtering, enhancement, restoration, rendering, halftoning, search, and analysis of images, video, and multidimensional signals.
Email Address. Sign In. Access provided by: Enter keywords or phrases Note: Searches metadata only by default. Enter keyword or title. Publication Title. When the Neon program is executed,. It is based on the statistical representation of a random variable.
This linear transform has been widely used in data analysis and compression. Impact of PCA is affecting the research work in. Many medical institutions do not have any dedicated computing infrastructure for research and a way to cope with this.
In this paper, a dot-matrix pattern is used to project a commercially-available low power.