This work presents a CMOS-compatible, energy-efficient, and trainable neuromorphic architecture employing LIF neurons, validated through mathematical analysis of the membrane potential dynamics. It is implemented in a modular$64\times 36$crossbar array for temporal sequential detection of 36 alphanumeric patterns, each represented by an$8\times 8$pixel input. A novel spike mismatch detection cell, implemented in UMC 65-nm CMOS technology, enables pixel-wise comparison using two mutually inhibiting LIF neurons and an output neuron that spikes only on mismatches. Column-wise mismatch spikes encode similarity, while a simplified analog winner-take-all (WTA) logic identifies the best-matching pattern and is capable of identifying both positional and density variations. Each modular cell occupies an area of approximately$608~\mu \text {m}^{2}$, with an average power of$25.82~\mu $W per column and energy of 64.55 pJ per inference (0–$2.5~\mu $s), and Monte Carlo simulations showed process/temperature robustness. An accuracy of >95% was achieved under 2–3 pixel perturbations. Real-time validation with fixed alphanumeric inputs confirmed reliable operation at up to 1 MHz with$0.25~\mu $s setup time. A hardware prototype for the proposed block using discrete ICs (SN7404, CD4007) further demonstrated precise spike generation and effective inhibition.

Age-related macular degeneration (AMD) is the leading cause of visual impairment among elderly in the world. Early detection of AMD is of great importance, as the vision loss caused by this disease is irreversible and permanent. Color fundus photography is the most cost-effective imaging modality to screen for retinal disorders. Cutting edge deep learning based algorithms have been recently developed for automatically detecting AMD from fundus images. However, there are still lack of a comprehensive annotated dataset and standard evaluation benchmarks. To deal with this issue, we set up the Automatic Detection challenge on Age-related Macular degeneration (ADAM), which was held as a satellite event of the ISBI 2020 conference. The ADAM challenge consisted of four tasks which cover the main aspects of detecting and characterizing AMD from fundus images, including detection of AMD, detection and segmentation of optic disc, localization of fovea, and detection and segmentation of lesions. As part of the ADAM challenge, we have released a comprehensive dataset of 1200 fundus images with AMD diagnostic labels, pixel-wise segmentation masks for both optic disc and AMD-related lesions (drusen, exudates, hemorrhages and scars, among others), as well as the coordinates corresponding to the location of the macular fovea. A uniform evaluation framework has been built to make a fair comparison of different models using this dataset. During the ADAM challenge, 610 results were submitted for online evaluation, with 11 teams finally participating in the onsite challenge. This paper introduces the challenge, the dataset and the evaluation methods, as well as summarizes the participating methods and analyzes their results for each task. In particular, we observed that the ensembling strategy and the incorporation of clinical domain knowledge were the key to improve the performance of the deep learning models.

We construe key factors in design and evaluation of image processing algorithms on the massive parallel graphics processing units (GPUs) using the compute unified device architecture (CUDA) programming model. A set of metrics, customized for image processing, is proposed to quantitatively evaluate algorithm characteristics. In addition, we show that a range of image processing algorithms map readily to CUDA using multiview stereo matching, linear feature extraction, JPEG2000 image encoding, and nonphotorealistic rendering (NPR) as our example applications. The algorithms are carefully selected from major domains of image processing, so they inherently contain a variety of subalgorithms with diverse characteristics when implemented on the GPU. Performance is evaluated in terms of execution time and is compared to the fastest host-only version implemented using OpenMP. It is shown that the observed speedup varies extensively depending on the characteristics of each algorithm. Intensive analysis is conducted to show the appropriateness of the proposed metrics in predicting the effectiveness of an application for parallel implementation.

In this paper, we present a framework for efficiently integrating programming resources of both GPU and CPU. We introduce an object oriented framework for GPGPU-based image processing. We illustrate a set of classes exploiting the design and programming advantages of an object oriented language, such as code reusability/extensibility, flexibility, information hiding, and complexity hiding. This class structure is supplemented with shader (GLSL) and kernel (CUDA) programming to facilitate full functionality. We demonstrate the potential of our approach with application scenarios and discuss the framework's performance in terms of programming effort, execution overhead, and speedup factor achieved over CPU.

The advent of GPUs with programmable shaders on handheld devices has motivated embedded application developers to utilize GPU to offload computationally intensive tasks and relieve the burden from embedded CPU. In this work, we propose an image processing toolkit on handheld GPU with programmable shaders using OpenGL ES 2.0 API. By using the image processing toolkit, we show that a range of image processing algorithms map readily to handheld GPU. We employ real-time video scaling, cartoon-style non-photorealistic rendering, and Harris corner detector as our example applications. In addition, we propose techniques to achieve increased performance with optimized shader design and efficient sharing of GPU workload between vertex and fragment shaders. Performance is evaluated in terms of frames per second at varying video stream resolution.

In this paper, we explore the key factors in the design and implementation of visual computing (image processing and computer vision) algorithms on the massive parallel GPU (graphics processing units). The goal of the exploration is to provide common perspective and guidelines of using GPU for visual computing applications. We have selected three nontrivial applications (multiview stereo matching, linear feature extraction, and JPEG2000 image encoding) for the benchmarks, which show different characteristics in GPU parallel computing. Intensive analysis is performed to evaluate the characteristic of each algorithm and its effect on the performance. Based on this, we draw general guidelines of using GPU for the visual computing algorithms.

Invariant image features can be used to carry watermarks so as to improve the robustness of the watermarks against geometric transformations. However, most previous watermarking algorithms using invariant features are still sensitive to cropping attacks and combinations of rotation, scaling, and translation (RST) attacks. To improve the resilience against these attacks, we propose a multi-bit image watermarking algorithm using local Zernike moments (LZMs). The magnitude of LZMs are dither-modulated to embed watermark bits. To achieve scale invariance, we restore the original sampling rate using invariant centroid and geometric moments. Simulation results demonstrate that the proposed watermarking algorithm is robust against various geometric attacks as well as signal processing attacks.