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Ear and Hearing, 1993
Evoked potential threshold estimation can be made truly objective by using statistically based methods. In general, time domain analysis is preferable for responses which are impulsive (temporally narrow, spectrally broad), whereas frequency-domain analysis is more appropriate for tonal responses (spectrally narrow, temporally broad).
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Evoked potential threshold estimation can be made truly objective by using statistically based methods. In general, time domain analysis is preferable for responses which are impulsive (temporally narrow, spectrally broad), whereas frequency-domain analysis is more appropriate for tonal responses (spectrally narrow, temporally broad).
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Camouflaged Object Detection with Feature Decomposition and Edge Reconstruction
Computer Vision and Pattern Recognition, 2023Camouflaged object detection (COD) aims to address the tough issue of identifying camouflaged objects visually blended into the surrounding backgrounds. COD is a challenging task due to the intrinsic similarity of camouflaged objects with the background,
Chunming He +6 more
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YOLOv10: Real-Time End-to-End Object Detection
Neural Information Processing SystemsOver the past years, YOLOs have emerged as the predominant paradigm in the field of real-time object detection owing to their effective balance between computational cost and detection performance.
Ao Wang +6 more
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Robust acoustic object detection
The Journal of the Acoustical Society of America, 2005We consider a novel approach to the problem of detecting phonological objects like phonemes, syllables, or words, directly from the speech signal. We begin by defining local features in the time-frequency plane with built in robustness to intensity variations and time warping.
Yali, Amit +2 more
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2009 Third International Conference on Advanced Engineering Computing and Applications in Sciences, 2009
Given a rectangle with emitters and receivers on its perimeter, one can detect objects in it by determining which of the line segments between emitters and receivers are blocked by objects. The problem of object detection can be formulated as the problem of finding all non-empty n-wedge intersections, where a wedge is defined by a consecutive set of ...
Jovanovic, N. +2 more
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Given a rectangle with emitters and receivers on its perimeter, one can detect objects in it by determining which of the line segments between emitters and receivers are blocked by objects. The problem of object detection can be formulated as the problem of finding all non-empty n-wedge intersections, where a wedge is defined by a consecutive set of ...
Jovanovic, N. +2 more
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YOLOv8: A Novel Object Detection Algorithm with Enhanced Performance and Robustness
2024 International Conference on Advances in Data Engineering and Intelligent Computing Systems (ADICS)In recent years, the You Only Look Once (YOLO) series of object detection algorithms have garnered significant attention for their speed and accuracy in real-time applications.
Rejin Varghese, S. M
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MODA: moving object detecting architecture
IEEE Transactions on Circuits and Systems II: Analog and Digital Signal Processing, 1993A type of cellular neural network (CNN) is described, which may be classified in the broader category of generalized cellular neural networks (GCNNs). Its novelty consists both in the task it performs and in its architecture and way of operation. The input to the network is a two-dimensional picture that is processed continuously in order to detect ...
CIMAGALLI V., BOBBI M., BALSI, Marco
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Performance evaluation of object detection techniques for object detection
2016 International Conference on Inventive Computation Technologies (ICICT), 2016Object detection plays vital role in image processing for finding the objects of interest Increase of image size and complexity has thrust for developing novel and robust object detection techniques. There are number of methods existing for detecting the objects in a particular scene.
M. N. Vijayalakshmi, M. Senthilvadivu
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De Computis
This paper provides a comprehensive review of the YOLO (You Only Look Once) framework up to its latest version, YOLO 11. As a state-of-the-art model for object detection, YOLO has revolutionized the field by achieving an optimal balance between speed and
Momina Liaqat Ali, Zhou Zhang
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This paper provides a comprehensive review of the YOLO (You Only Look Once) framework up to its latest version, YOLO 11. As a state-of-the-art model for object detection, YOLO has revolutionized the field by achieving an optimal balance between speed and
Momina Liaqat Ali, Zhou Zhang
semanticscholar +1 more source