Object recognition in images using convolutional neural network

Sudharshan, Duth P.; Raj, Swathi

doi:10.1109/icisc.2018.8398893

Cited by 26 publications

(9 citation statements)

References 5 publications

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“…Detection of objects from the image repository is a difficult task in the computer vision area [74]. Sudharshan and Raj [40] have presented a CNN on Keras for detection and classification of the object. The CIFAR 10 dataset with 60,000 images is trained to the system for detection.…”

Section: Object Recognitionmentioning

confidence: 99%

A Review on Neural Approaches in Image Processing Applications

Bhalla¹

2023

IJRASET

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Image processing is the manipulation and analysis of a digitalized image; it especially improves the image quality. Also, it yields indispensable facts about the image processing techniques required for image enhancement, restoration, preprocessing, and segmentation. These methods help to provide earlier object detection and prevent further impacts due to segmentation and classification. Many reviews already exist for this problem, but those reviews have presented the analysis of a single framework. Hence, this article on Machine Learning (ML) in image processing review has revealed distinct methodologies with diverse frameworks utilized for object detection. The novelty of this review research lies in finding the best neural network model by comparing its efficiency. For that, ML approaches were compared and reported as the best model. Moreover, different kinds of datasets were used to detect the objects and unknown users or intruders. The execution of each approach is compared based on the performance metrics such as sensitivity, specificity, and accuracy using publically accessible datasets like STARE, DRIVE, ROSE, BRATS, and ImageNet. This article discloses the implementation capacity of distinct techniques implemented for each processing methods like supervised and unsupervised. Finally, the Naïve Bayes and LMS model achieved 100% accuracy as finest. Moreover, this technique has utilized public datasets to verify the efficiency. Hence, the overall review of this article has revealed a method for detecting images effectively.

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Section: Object Recognitionmentioning

confidence: 99%

A Review on Neural Approaches in Image Processing Applications

Bhalla¹

2023

IJRASET

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“…The Convolutional Neural Network (CNN) [34] is the most widely used technology for improving picture categorization accuracy. CNN is a worldwide utilized image processing and pattern recognition technique that is efficient and successful in recognition, identification, and classification [35].…”

Section: Related Workmentioning

confidence: 99%

HActivityNet: A Deep Convolutional Neural Network for Human Activity Recognition

Khaliluzzaman

Sayem

KaderMisbah

2021

EMITTER Int'l J. of Engin. Technol.

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Human Activity Recognition (HAR), a vast area of a computer vision research, has gained standings in recent years due to its applications in various fields. As human activity has diversification in action, interaction, and it embraces a large amount of data and powerful computational resources, it is very difficult to recognize human activities from an image. In order to solve the computational cost and vanishing gradient problem, in this work, we have proposed a revised simple convolutional neural network (CNN) model named Human Activity Recognition Network (HActivityNet) that is automatically extract and learn features and recognize activities in a rapid, precise and consistent manner. To solve the problem of imbalanced positive and negative data, we have created two datasets, one is HARDataset1 dataset which is created by extracted image frames from KTH dataset, and another one is HARDataset2 dataset prepared from activity video frames performed by us. The comprehensive experiment shows that our model performs better with respect to the present state of the art models. The proposed model attains an accuracy of 99.5% on HARDatase1 and almost 100% on HARDataset2 dataset. The proposed model also performed well on real data.

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“…A CNN consists of an input and an output layer and multiple hidden layers. The hidden layers are consisting of convolutional layers, pooling layers, fully connected layers [26].…”

Section: Feature Matchingmentioning

confidence: 99%

Detection and Recognition of Bangladeshi Fishes Using Surf and Convolutional Neural Network.

Pavel¹

2019

IJAR

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This paper represents a model to detection and recognize local fishes of Bangladesh implementing image processing and neural networking approaches. The aim of the research work is to apply computer vision and AI techniques so that people of next generation can recognize Bangladeshi fishes as most of the young people in city, have less idea to classify traditional and deshi fishes. We implemented our custom Dataset consisting of 400 sample images for the experiment method to measure out its credibility. In the proposed, model sequential grassfire algorithm is used along with pre-processing techniques like noise cancelation, gray scaling, flood-fill method, binarization to detect and analysis shape of fish. Further, to do classification and recognition of the detected fishes, convolutional neural network (CNN) and method of Speeded up robust feature (SURF) had been applied to visualize difference between to techniques. CNN architecture got better accuracy with 90.9% score for recognition and classify fishes where SURF algorithm visualize better recognition matching putative points after extracting features.

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Object recognition in images using convolutional neural network

Cited by 26 publications

References 5 publications

A Review on Neural Approaches in Image Processing Applications

A Review on Neural Approaches in Image Processing Applications

HActivityNet: A Deep Convolutional Neural Network for Human Activity Recognition

Detection and Recognition of Bangladeshi Fishes Using Surf and Convolutional Neural Network.

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