Search Results for "pixel"

The Simd Library is a free open-source image processing and machine learning library, designed for C and C++ programmers. It provides many useful high-performance algorithms for image processing such as pixel format conversion, image scaling and filtration, extraction of statistical information from images, motion detection, object detection and classification, neural networks. The algorithms are optimized with using of different SIMD CPU extensions. In particular, the library supports the following CPU extensions: SSE, AVX, AVX-512, and AMX for x86/x64, and NEON for ARM. ...

...Conditional Random Fields are probabilistic graphical models used to model contextual relationships between neighboring pixels or features, improving prediction consistency across images. By implementing a fully connected CRF model with Gaussian edge potentials, the library enables efficient inference across all pixel pairs in an image rather than only local neighborhoods. The Python wrapper is implemented using Cython, allowing high-performance CRF computations while maintaining a Python-friendly interface for experimentation and development.

Fast face detection, pupil/eyes localization and facial landmark points detection library in pure Go. Pigo is a pure Go face detection, pupil/eyes localization and facial landmark points detection library based on the Pixel Intensity Comparison-based Object detection paper. The reason why Pigo has been developed is because almost all of the currently existing solutions for face detection in the Go ecosystem are purely bindings to some C/C++ libraries like OpenCV or dlib, but calling a C program through cgo introduces huge latencies and implies a significant trade-off in terms of performance. ...

...It may happen that during propagation all the coefficients for a certain pixel become zero. That means that the algorithm has run into a contradiction and can not continue. The problem of determining whether a certain bitmap allows other nontrivial bitmaps satisfying condition (C1) is NP-hard, so it's impossible to create a fast solution that always finishes. In practice, however, the algorithm runs into contradictions surprisingly rarely.

Fashion-MNIST is an open-source dataset created by Zalando Research that provides a standardized benchmark for image classification algorithms in machine learning. The dataset contains grayscale images of fashion products such as shirts, shoes, coats, and bags, each labeled according to its clothing category. It was designed as a direct replacement for the original MNIST handwritten digits dataset, maintaining the same structure and image size so that researchers could easily switch datasets...

It can predict digits from 0-9 with Artificial Neural Network. I trained ANN with 100 samples of each digit. It takes input of 20x20 pixel image and predicts it with Neural Network. It may predict wrong digit due to very low sample data but it work 90% correctly. Note: JRE 1.6 is required to run this application.