Perception is the process by which physical energy from the environment is converted into electrochemical energy by the senses and processed by the brain for interaction with the w... Perception is the process by which physical energy from the environment is converted into electrochemical energy by the senses and processed by the brain for interaction with the world. Different senses such as vision, audition, touch, gustation, and olfaction play a role in perception. The eye is compared to a camera and consists of various components such as rods, cones, and photoreceptors. Parallel processing streams in the eye include magnocellular and parvocellular pathways for different functions. The dorsal stream is responsible for 'where' vision, while the ventral stream is responsible for 'what' vision. Historical perspective on perception includes factors like surface perception, scene perception, and depth perception using cues. Depth perception cues include atmospheric perspective, occlusion, linear perspective, and texture. Size and distance perception can be influenced by factors like Emmert's Law and illusions such as the Ames Room. Shading and other visual cues also play a role in perception. Detailed Summary: Shadows and Seeing Depth: Cue Approach for Motion in Depth. Includes binocular stereopsis, disparity, and oculomotor cues. Muscle proprioception plays a role. Perceiving Objects and Forms: Gestalt Approach with principles of perceptual organization. Focus on figure-ground separation. Perceptual and Computational Processing Theories: Marr's bottom-up analysis and Biederman's Recognition by Components. Bayesian models for top-down and bottom-up processing. Convolutional Neural Networks for object recognition. Object and Shape Recognition Theories: Direct analysis of shapes and structural descriptions. Challenges include identifying parts, viewing angles, and photometric issues. Different approaches like bottom-up analysis and Recognition by Components. Geons and Image-Based Models: Recognition by components using geons. Image-based models with interpolation techniques. Special or canonical views are stored for recognition. Bayesian Models and Convolutional Neural Networks: Bayesian models for object recognition. Convolutional Neural Networks for deep learning in object recognition. Analysis by synthesis approach for generative models. Attention and Visual Effects: Visual effects and consciously processed scenes. Study by Simons & Levin on change detection in visual scenes. Change Detection in Visual Scenes: People often fail to detect changes in visual scenes. Factors like age change and status change affect detection rates. Representation of Visual Events: Question raised on what exactly is represented about visual events. Convolutional Neural Network Structure: Input layer, convolution layer, sub-sampling layer, and fully connected MLP. Breakthrough in object recognition with deep convolutional neural networks. Read more