Object recognition through pose and shape estimation
- URL: http://arxiv.org/abs/2006.12864v1
- Date: Tue, 23 Jun 2020 09:55:40 GMT
- Title: Object recognition through pose and shape estimation
- Authors: Anitta D, Annis Fathima A
- Abstract summary: Computer vision helps machines or computer to see like humans.
Gesture recognition and movement recognition are the current area of research in computer vision.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Computer vision helps machines or computer to see like humans. Computer Takes
information from the images and then understands of useful information from
images. Gesture recognition and movement recognition are the current area of
research in computer vision. For both gesture and movement recognition finding
pose of an object is of great importance. The purpose of this paper is to
review many state of art which is already available for finding the pose of
object based on shape, based on appearance, based on feature and comparison for
its accuracy, complexity and performance
Related papers
- Interpreting Hand gestures using Object Detection and Digits Classification [0.0]
This research aims to develop a robust system that can accurately recognize and classify hand gestures representing numbers.
The proposed approach involves collecting a dataset of hand gesture images, preprocessing and enhancing the images, extracting relevant features, and training a machine learning model.
arXiv Detail & Related papers (2024-07-15T16:53:04Z) - CLiC: Concept Learning in Context [54.81654147248919]
This paper builds upon recent advancements in visual concept learning.
It involves acquiring a visual concept from a source image and subsequently applying it to an object in a target image.
To localize the concept learning, we employ soft masks that contain both the concept within the mask and the surrounding image area.
arXiv Detail & Related papers (2023-11-28T01:33:18Z) - Understanding Self-Supervised Pretraining with Part-Aware Representation
Learning [88.45460880824376]
We study the capability that self-supervised representation pretraining methods learn part-aware representations.
Results show that the fully-supervised model outperforms self-supervised models for object-level recognition.
arXiv Detail & Related papers (2023-01-27T18:58:42Z) - Peripheral Vision Transformer [52.55309200601883]
We take a biologically inspired approach and explore to model peripheral vision in deep neural networks for visual recognition.
We propose to incorporate peripheral position encoding to the multi-head self-attention layers to let the network learn to partition the visual field into diverse peripheral regions given training data.
We evaluate the proposed network, dubbed PerViT, on the large-scale ImageNet dataset and systematically investigate the inner workings of the model for machine perception.
arXiv Detail & Related papers (2022-06-14T12:47:47Z) - Embodied vision for learning object representations [4.211128681972148]
We show that visual statistics mimicking those of a toddler improve object recognition accuracy in both familiar and novel environments.
We argue that this effect is caused by the reduction of features extracted in the background, a neural network bias for large features in the image and a greater similarity between novel and familiar background regions.
arXiv Detail & Related papers (2022-05-12T16:36:27Z) - Muscle Vision: Real Time Keypoint Based Pose Classification of Physical
Exercises [52.77024349608834]
3D human pose recognition extrapolated from video has advanced to the point of enabling real-time software applications.
We propose a new machine learning pipeline and web interface that performs human pose recognition on a live video feed to detect when common exercises are performed and classify them accordingly.
arXiv Detail & Related papers (2022-03-23T00:55:07Z) - Detection, Recognition, and Tracking: A Survey [0.0]
In Computer Vision and Multimedia, it is increasingly important to detect, recognize and track objects in images and/or videos.
Many applications, such as facial recognition, surveillance, animation, are used for tracking features and/or people.
This literature review focuses on some novel techniques on object detection and recognition, and how to apply tracking algorithms to the detected features to track the objects' movements.
arXiv Detail & Related papers (2022-03-22T17:11:24Z) - Attention Mechanisms in Computer Vision: A Survey [75.6074182122423]
We provide a comprehensive review of various attention mechanisms in computer vision.
We categorize them according to approach, such as channel attention, spatial attention, temporal attention and branch attention.
We suggest future directions for attention mechanism research.
arXiv Detail & Related papers (2021-11-15T09:18:40Z) - Capturing the objects of vision with neural networks [0.0]
Human visual perception carves a scene at its physical joints, decomposing the world into objects.
Deep neural network (DNN) models of visual object recognition, by contrast, remain largely tethered to the sensory input.
We review related work in both fields and examine how these fields can help each other.
arXiv Detail & Related papers (2021-09-07T21:49:53Z) - A Review on Near Duplicate Detection of Images using Computer Vision
Techniques [0.0]
The presence of near-duplicates affects the performance of the search engines critically.
The main application of computer vision is image understanding.
There is no proper survey in literature related to near duplicate detection of images.
arXiv Detail & Related papers (2020-09-07T16:41:46Z) - A Review on Intelligent Object Perception Methods Combining
Knowledge-based Reasoning and Machine Learning [60.335974351919816]
Object perception is a fundamental sub-field of Computer Vision.
Recent works seek ways to integrate knowledge engineering in order to expand the level of intelligence of the visual interpretation of objects.
arXiv Detail & Related papers (2019-12-26T13:26:49Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.