Feature detection for color images using SURF

Muthugnanambika, M; Padmavathi, S

doi:10.1109/icaccs.2017.8014572

Cited by 14 publications

(3 citation statements)

References 4 publications

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“…SURF can be partitioned into three steps [40]. The initial step determines the interest points of an image I at ant any point x and any angle σ by using Hessian matrix H (I, σ) in equation (7).…”

Section: Detection Of Silent Objectsmentioning

confidence: 99%

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Interpretation of Spatial Relationships by Objects Tracking in a Complex Streaming Video

Alabid

2021

ECTI-CIT

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By interpreting spatial relations among objects, many applications such as video surveillance, robotics, and scene understanding systems can be utilized efficiently for different purposes. The vast majority of known models for spatial relationships are carried out with an image. However, due to the advance in technology, a three-dimensional scene became available. For our knowledge, most of the interpreted spatial relations were defined between silent objects in images. A technique for determining the dynamic spatial relation between a moving object and another silent one in a time varying scene is presented here. The spatial relationships were determined by using motion-based object tracking along with hypergraph object-oriented model. Defining the spatial relationship types between a single silent object and a moving human body has applied based on two strategies; determining each object with a bounding box, then comparing the locations of these boxes by applying certain conditional rules. This study identifies some of the spatial relationships in three dimensions of streaming frames, which has carried out by establishing a highly accurate and efficient proposed algorithm. The following relations have been studied; (“direct in front of”, “in front of on the Right/Left”, “direct behind of”, “behind of on the Right/Left”, “to the Right”, “to the Left”, “On”, “Under”, Besides, and “Besides on to the Right/Left”). The experimental results, which have been obtained based on actual indoor streaming frames, show effectiveness and reliable execution of our system

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Section: Detection Of Silent Objectsmentioning

confidence: 99%

“…The last step is implemented by executing a comparing operation between features to detect the identical pair of features between different images [40]. Here, once the system is run, both images that include silent object and cluttered scene (frame from the video) are read in a grayscale image type.…”

Section: Detection Of Silent Objectsmentioning

confidence: 99%

Interpretation of Spatial Relationships by Objects Tracking in a Complex Streaming Video

Alabid

2021

ECTI-CIT

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“…Step iv: Hessian based blob detector is used in Speeded-Up Robust Features to find interest points the function is called FastHessian.m. The determinant of a hessian matrix expresses the extent of the response and is an expression of the local change around the area [29].…”

Section: Feature Extractionmentioning

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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