8 Object Recognition Books That Separate Experts from Amateurs

Rowel Atienza brings a wealth of academic and research experience to this detailed exploration of advanced deep learning techniques using TensorFlow 2 and Keras. You’ll move beyond basics to tackle multi-layer perceptrons, convolutional and recurrent networks, and then dive into generative adversarial networks (GANs), variational autoencoders (VAEs), and deep reinforcement learning methods. Chapters on object detection and semantic segmentation provide concrete frameworks for practical AI applications in computer vision. This book suits you if you already know Python and have some machine learning background but want to elevate your skills to cutting-edge AI projects.

Drawing from his extensive research background in machine learning and deep learning, Van Vung Pham offers a focused exploration of Detectron2, Facebook's advanced library for object detection and segmentation. You learn not just theory but practical skills, such as data preparation, model training, fine-tuning, and deployment into production and mobile environments. The book dives deep into Detectron2's architecture, with chapters dedicated to performance tuning and image augmentation techniques that sharpen model accuracy. It's particularly suited for developers and researchers who want hands-on experience with cutting-edge computer vision tools, though some programming knowledge enhances the experience.

This personalized book explores deep learning techniques specifically tailored for object recognition, focusing on approaches that align with your background and goals. It reveals how convolutional neural networks and feature extraction methods can be applied effectively to real-world AI challenges. By concentrating on your interests, the book navigates through complex architectures, training processes, and optimization techniques that enhance recognition accuracy. Tailored content ensures that you engage with concepts and examples most relevant to your experience, offering a customized pathway through advanced material. Whether refining skills or building new expertise, this resource fosters a deep understanding of object recognition's dynamic landscape, blending foundational theory with practical applications.

Drawing from his extensive international research and numerous patents, Benjamin Planche offers a detailed exploration of deep learning applied to computer vision using TensorFlow 2. You’ll learn to build, train, and deploy convolutional neural networks, mastering architectures like Inception and ResNet, and techniques such as YOLO for object detection and Mask R-CNN for segmentation. The book also guides you through generative adversarial networks and recurrent neural networks for video analysis, with practical examples on mobile and web deployment. Whether you’re new to deep learning with some Python background or an expert exploring TensorFlow 2’s features, this book equips you with the skills to develop advanced image processing applications.

Drawing from his deep expertise in computer vision, Sanath Shenoy examines how deep learning models perform object detection under challenging low-light conditions. You gain insight into the impact of poor lighting on detection accuracy and explore specific image enhancement techniques that can improve results. His work demystifies the technical nuances of adapting standard models to less-than-ideal environments, making it especially relevant if you work with real-world image data where lighting cannot be controlled. This book is best suited for practitioners and researchers aiming to enhance object detection reliability in difficult visual settings.

Unlike most object recognition books that skim theory, this one dives deep into building real projects using OpenCV 4 and C++. The authors, experienced developers and researchers, guide you through practical tasks like image segmentation, motion detection, and text recognition with Tesseract, supported by hands-on code examples. You’ll gain skills in advanced techniques such as 3D scene reconstruction, background subtraction, and deep learning integration within OpenCV, making it ideal if you want to create functional computer vision applications from scratch. This book suits software developers with some familiarity in C++ and image processing who want to move beyond basics and tackle complex computer vision challenges.

This tailored book explores the essential steps to build and deploy object recognition systems within a focused 90-day period. It covers foundational concepts in computer vision and machine learning, progressing through designing, training, and optimizing models specifically for your background and goals. The content examines practical challenges and solutions in object recognition, offering a personalized pathway that matches your experience level and targeted applications. By concentrating on your interests, the book facilitates efficient learning and hands-on skill development in object recognition technologies. It provides a clear, tailored roadmap to accelerate your understanding and system deployment in real-world contexts.

After extensive research in medical image computing, S. Kevin Zhou developed this book to address the complex challenge of automatically recognizing and parsing multiple anatomical structures within medical images. You gain detailed insights into machine learning techniques tailored to segmenting and analyzing cohorts of anatomical objects, supported by large datasets and practical algorithms. Chapters delve into cutting-edge methods for efficient image parsing and contextual anatomical recognition, making the content especially useful if you’re involved in medical imaging research or developing clinical applications. While highly specialized, it’s best suited for professionals and academics wanting to deepen their understanding of multi-object recognition in medical contexts.

When Glyn W. Humphreys and Jane M. Riddoch developed BORB, their decades of research in cognitive neuropsychology shaped a tool that rigorously dissects visual object recognition disorders. This book guides you through standardized tests evaluating everything from basic visual perception, like orientation and size matching, to complex processes such as semantic access and object naming. You’ll gain detailed insights into how these assessments reveal different layers of visual cognition, useful for clinicians and researchers focused on neuropsychological diagnosis. While dense in methodology, it offers practical frameworks for interpreting results and connecting them to cognitive theory, making it particularly relevant if you work with visual perception impairments or neuropsychological assessments.

Florian Dedov brings together his dual expertise in computer science and economics to deliver a focused guide on computer vision using Python. This volume walks you through core techniques such as color space transformations, edge detection, template matching, and real-time object recognition with OpenCV, making complex topics approachable in just 67 pages. You’ll learn practical skills like enhancing poorly lit text, detecting motion in videos, and extracting meaningful information from images, all framed within applied programming projects. If you’re aiming to build solid foundational skills in computer vision without getting lost in overwhelming theory, this book offers a concise, hands-on path that’s especially suited for self-learners and developers looking to integrate visual computing into their workflows.