OPENCV FOR VISION ENGINEERS : PRODUCTION COMPUTER VISION WITH PYTHON AND C++: Object Detection, 3D Vision, Camera Calibration, Feature Matching, Real-Time Tracking, and Edge Deployment
Language: English
Published by Independently Published Sep 2026, 2026
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Neuware - Build computer vision systems that move beyond demos and stand up to real production demands.Computer vision becomes harder when you leave controlled examples behind. Cameras introduce timing, exposure, distortion, buffering, and synchronization problems. Neural networks bring preprocessing contracts, runtime compatibility, hardware acceleration, and deployment constraints. Tracking, stereo vision, feature matching, and calibration add their own geometric and numerical failure modes.This practical guide shows you how to design reliable vision pipelines with Python and C++, connect OpenCV algorithms into complete systems, measure what matters, and make informed engineering decisions from image acquisition through inference, tracking, 3D reconstruction, optimization, testing, and edge deployment.- Work confidently with cv Mat NumPy arrays memory ownership strides ROIs image types color spaces and coordinate transformations- Build reliable camera pipelines for video files RTSP streams industrial cameras Bayer data hardware triggers timestamps buffering and multi camera synchronization- Design real time capture processing inference tracking and output pipelines with bounded queues backpressure recovery and controlled latency- Deploy ONNX object detection models with correct letterboxing normalization tensor layouts coordinate recovery confidence filtering and NMS- Use SIFT ORB FAST GFTT MSER ALIKED DISK LightGlue brute force matching FLANN and approximate nearest neighbor search- Apply homographies affine transforms epipolar geometry RANSAC USAC PROSAC MAGSAC++ PnP triangulation and coordinate frame composition- Calibrate real camera systems with chessboards circle grids ArUco ChArUco fisheye stereo multi camera and hand eye workflows- Generate disparity metric depth point clouds and 3D reconstructions using StereoBM StereoSGBM triangulation ICP RGB D methods and TSDF concepts- Implement motion analysis optical flow single object tracking Kalman filtering data association SORT Deep SORT and ByteTrack concepts- Profile CPU memory allocation cache behavior threading SIMD OpenCL CUDA GpuMat streams pinned memory and data transfer costs- Choose between OpenCV DNN ONNX Runtime TensorRT and OpenVINO while working with FP32 FP16 BF16 INT8 and quantized models- Deploy vision workloads to Jetson Intel accelerators Raspberry Pi ARM systems and on sensor inference platforms- Test production systems with golden images numerical tolerances regression tests soak tests failure injection health checks metrics model provenance and calibration versioningThroughout the guide, working Python and C++ code shows how the concepts translate into practical implementations, from camera acquisition and feature matching to neural inference, stereo processing, tracking, profiling, testing, and production service architecture.Grab your copy today and start building computer vision systems designed for accuracy, performance, reliability, and real deployment.…
Seller Inventory # 9798175292382
- Title
- OPENCV FOR VISION ENGINEERS : PRODUCTION COMPUTER VISION WITH PYTHON AND C++: Object Detection, 3D Vision, Camera Calibration, Feature Matching, Real-Time Tracking, and Edge Deployment
- Author
- Jules Calderon
- Publisher
- Independently Published Sep 2026
- Publication year
- 2026
- Condition
- Neu
- Binding
- Taschenbuch
- Language
- English
- ISBN 13
- 9798175292382
- Item weight
- 676 grams
- Dimensions
- 254x178x20 mm
Build computer vision systems that move beyond demos and stand up to real production demands.
Computer vision becomes harder when you leave controlled examples behind. Cameras introduce timing, exposure, distortion, buffering, and synchronization problems. Neural networks bring preprocessing contracts, runtime compatibility, hardware acceleration, and deployment constraints. Tracking, stereo vision, feature matching, and calibration add their own geometric and numerical failure modes.
This practical guide shows you how to design reliable vision pipelines with Python and C++, connect OpenCV algorithms into complete systems, measure what matters, and make informed engineering decisions from image acquisition through inference, tracking, 3D reconstruction, optimization, testing, and edge deployment.
- Work confidently with cv Mat NumPy arrays memory ownership strides ROIs image types color spaces and coordinate transformations
- Build reliable camera pipelines for video files RTSP streams industrial cameras Bayer data hardware triggers timestamps buffering and multi camera synchronization
- Design real time capture processing inference tracking and output pipelines with bounded queues backpressure recovery and controlled latency
- Deploy ONNX object detection models with correct letterboxing normalization tensor layouts coordinate recovery confidence filtering and NMS
- Use SIFT ORB FAST GFTT MSER ALIKED DISK LightGlue brute force matching FLANN and approximate nearest neighbor search
- Apply homographies affine transforms epipolar geometry RANSAC USAC PROSAC MAGSAC++ PnP triangulation and coordinate frame composition
- Calibrate real camera systems with chessboards circle grids ArUco ChArUco fisheye stereo multi camera and hand eye workflows
- Generate disparity metric depth point clouds and 3D reconstructions using StereoBM StereoSGBM triangulation ICP RGB D methods and TSDF concepts
- Implement motion analysis optical flow single object tracking Kalman filtering data association SORT Deep SORT and ByteTrack concepts
- Profile CPU memory allocation cache behavior threading SIMD OpenCL CUDA GpuMat streams pinned memory and data transfer costs
- Choose between OpenCV DNN ONNX Runtime TensorRT and OpenVINO while working with FP32 FP16 BF16 INT8 and quantized models
- Deploy vision workloads to Jetson Intel accelerators Raspberry Pi ARM systems and on sensor inference platforms
- Test production systems with golden images numerical tolerances regression tests soak tests failure injection health checks metrics model provenance and calibration versioning
Throughout the guide, working Python and C++ code shows how the concepts translate into practical implementations, from camera acquisition and feature matching to neural inference, stereo processing, tracking, profiling, testing, and production service architecture.
Grab your copy today and start building computer vision systems designed for accuracy, performance, reliability, and real deployment.
"Synopsis" may belong to another edition of this title.
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