AIMC Topic: Pattern Recognition, Visual

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DCTCNet: Sequency discrete cosine transform convolution network for visual recognition.

Neural networks : the official journal of the International Neural Network Society
The discrete cosine transform (DCT) has been widely used in computer vision tasks due to its ability of high compression ratio and high-quality visual presentation. However, conventional DCT is usually affected by the size of transform region and res...

Exploring neural architectures for simultaneously recognizing multiple visual attributes.

Scientific reports
Much experimental evidence in neuroscience has suggested a division of higher visual processing into a ventral pathway specialized for object recognition and a dorsal pathway specialized for spatial recognition. Previous computational studies have su...

Parallel development of object recognition in newborn chicks and deep neural networks.

PLoS computational biology
How do newborns learn to see? We propose that visual systems are space-time fitters, meaning visual development can be understood as a blind fitting process (akin to evolution) in which visual systems gradually adapt to the spatiotemporal data distri...

Spike-VisNet: A novel framework for visual recognition with FocusLayer-STDP learning.

Neural networks : the official journal of the International Neural Network Society
Current vision-inspired spiking neural networks (SNNs) face key challenges due to their model structures typically focusing on single mechanisms and neglecting the integration of multiple biological features. These limitations, coupled with limited s...

Plant-based egg washes for use in baked goods: Machine learning and visual parameter analysis.

Journal of food science
Pea protein is one potential environmentally sustainable way of recreating the functionality of eggs in coatings for baked goods. These coatings are commonly applied to enhance visual properties of baked goods that consumers desire, especially color ...

Visual search and real-image similarity: An empirical assessment through the lens of deep learning.

Psychonomic bulletin & review
The ability to predict how efficiently a person finds an object in the environment is a crucial goal of attention research. Central to this issue are the similarity principles initially proposed by Duncan and Humphreys, which outline how the similari...

Bio-inspired deep neural local acuity and focus learning for visual image recognition.

Neural networks : the official journal of the International Neural Network Society
In the field of computer vision and image recognition, enabling the computer to discern target features while filtering out irrelevant ones poses a challenge. Drawing insights from studies in biological vision, we find that there is a local visual ac...

Cracking the neural code for word recognition in convolutional neural networks.

PLoS computational biology
Learning to read places a strong challenge on the visual system. Years of expertise lead to a remarkable capacity to separate similar letters and encode their relative positions, thus distinguishing words such as FORM and FROM, invariantly over a lar...

The Quest for an Integrated Set of Neural Mechanisms Underlying Object Recognition in Primates.

Annual review of vision science
Inferences made about objects via vision, such as rapid and accurate categorization, are core to primate cognition despite the algorithmic challenge posed by varying viewpoints and scenes. Until recently, the brain mechanisms that support these capab...

Effects of machine learning errors on human decision-making: manipulations of model accuracy, error types, and error importance.

Cognitive research: principles and implications
This study addressed the cognitive impacts of providing correct and incorrect machine learning (ML) outputs in support of an object detection task. The study consisted of five experiments that manipulated the accuracy and importance of mock ML output...