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The transport of intensity equation (TIE) has established the relationship between the phase of an optical field and the variation of its intensity along the direction of propagation. In this paper, a new method based on TIE used for two-wavelength phase retrieval has been proposed. The TIE is solved by employing the regularized Fourier-transform-based-approach under two separate wavelengths, gaining...
The development of systems used to increase the energy efficiency of premises has a broad ecological and commercial importance. The most significant savings are achieved by managing lighting. There are different types of systems used for lighting control: time switches, photocells, etc. The prototype of the lighting management system that has the possibility to use several algorithms for lighting...
In most convolutional neural networks (CNNs), the output is a single classification result by combining all the neuron activations in the last layer. As we know, local connectivity is an important characteristic of CNNs. Each neuron in the network corresponds to a local region in the original image. Hence, it is possible to simultaneously obtain local visibility of a target object by analyzing neuron...
Solid-state lighting is a field that has developed rapidly over the recent years. Visible Light Emitting Diode (LED) has achieved higher luminous efficacy than that of incandescent light, subsequently incorporated into many lighting systems in household or in workplaces. Energy saving can be achieved by utilizing adaptive lighting systems which can adapt to ambient lighting. We discuss the ability...
License Plate Detection (LPD) is the pivotal step for License Plate Recognition. In this work, we explore and customize state-of-the-art detection approaches for exclusively handling the LPD in the wild. In-the-wild LPD considers license plates captured in challenging conditions caused by bad weathers, lighting, traffics, and other factors. As conventional methods failed to handle these inevitable...
This research proposes a reliable machine learning based computational solution for human detection. The proposed model is specifically applicable for illumination-variant natural scenes in big data video frames. In order to solve the illumination variation problem, a new feature set is formed by extracting features using histogram of gradients (HoG) and linear phase quantization (LPQ) techniques,...
This paper presents a novel method for detecting pedestrians under adverse illumination conditions. Our approach relies on a novel cross-modality learning framework and it is based on two main phases. First, given a multimodal dataset, a deep convolutional network is employed to learn a non-linear mapping, modeling the relations between RGB and thermal data. Then, the learned feature representations...
Real-time human detection is important for a wide range of applications. In this paper, a two-staged method has been developed for real-time human detection in cluttered and dynamic environments with depth data. We start with generating a set of possible human head-tops to ensure all human locations are included. To this end, a novel physical radius-depth (PRD) detector is proposed to quickly detect...
The goal of tone mapping operators (TMOs) has traditionally been to display high dynamic range (HDR) pictures in a perceptually favorable way. However, when tone-mapped images are to be used for computer vision tasks such as keypoint detection, these design approaches are suboptimal. In this paper, we propose a new learning-based adaptive tone mapping framework which aims at enhancing keypoint stability...
Active spectral imaging in the mid-infrared can reveal absorption ‘fingerprints’ of different chemicals within a scene. The practicality of spectral imaging with active illumination is limited by the large number of images required (as the illumination wavelength or an interferometer mirror is scanned). Single pixel imaging uses the intensity reading from a single pixel detector while illuminating...
Foreground detection is one of the well and widely studied research topic in the field of computer vision. However, it still fails to cope with the many practical issues such as illumination changes, dynamic backgrounds, and shadow. This paper proposes optimal color space based probabilistic foreground detector. The intuition is to employ two most widely used color spaces (RGB and YCbCr) one at a...
The interest for more digital functionality in the readout circuits for imagers is growing rapidly. Similarly, there are advantages to having the pixel pitch smaller from visible to long wave IR. The front end is the dominant source of electronic noise for an in-pixel digital design. Limiting the real estate considerably would force the design to smaller feature size, which may worsen the random telegraph...
This paper presents a camera prototype for 2D/3D image capture in low illumination conditions based on single-photon avalanche-diode (SPAD) image sensor for direct time-offlight (d-ToF). The imager is a 64×64 array with in-pixel TDC for high frame rate acquisition. Circuit design techniques are combined to ensure successful 3D image capturing under low sensitivity conditions and high level of uncorrelated...
A primary-side output current estimator with process compensator for the flyback control circuits is designed and analyzed in this investigation. Flyback control circuits play a key role of smart lighting systems, where LED drivers require a compensator to keep the system stable. The process sensor is in charge of the detection of the chip process corner. Then, the process compensator correspondingly...
This paper presents a novel appearance and shape feature, RISAS, which is robust to viewpoint, illumination, scale and rotation variations. RISAS consists of a keypoint detector and a feature descriptor both of which utilise texture and geometric information present in the appearance and shape channels. A novel response function based on the surface normals is used in combination with the Harris corner...
Digital image feature detection and matching is an important research content in the field of computer vision and pattern recognition. Inspired by Harris corner detector, the Harris correlation detector and Harris correlation descriptor (HCD) was studied and improved. In this paper, the scale adaptive Gaussian filter was introduced to optimize the descriptor and then the RANSAC algorithm was used...
We present a new image feature detection method. Our method selects features based on segmenting points with high local intensity variations across different scales using a robust rank order statistics approach. Our method produces a large number of repeatable features that are invariant to several image transformations such as rotation, scaling, viewpoint, and lighting variations. We show the advantages...
This paper proposes a novel inherently rotation invariant local descriptor which combined intensity information and gradient information of key feature. The CS-LBP shows a better performance than SIFT and do not need large computation. To further enhance its performance and robustness, we calculated the gradient of key feature and computed a combined histogram included intensity and gradient information...
Fast and accurate detection of human skin color is an important task in computer vision and image processing applications. Skin color detection algorithms are vital in medical application, especially in diagnosing skin diseases. This paper presents an approach for defining an explicit skin model by determining the optimal skin color regions in the selected color space. During the optimization, the...
Feature refers to some relevant information which is present on images or faces. Feature extraction used to extract those features from the face. Among that bulk of keypoints, only robust features are detected by using feature descriptors. This paper analyzes 2 robust feature detector and descriptors are: Scale-Invariant Feature Transform (SIFT) and Speeded Up Robust Features (SURF). These two robust...
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