ICIP 2021 - The International Conference on Image Processing (ICIP), sponsored by the IEEE Signal Processing Society, is the premier forum for the presentation of technological advances and research results in the fields of theoretical, experimental, and applied image and video processing. ICIP has been held annually since 1994, brings together leading engineers and scientists in image and video processing from around the world. Visit website.
A new breakpoint adaptive DWT, referred to as tri-break, is currently
being considered by standardization efforts in relation to JPEG 2000
Part 17 extensions. We first provide a summary of the tri-break transform
and then explore its performance for coding motion fields. Experimental
results show that significant gains can be achieved for
coding piecewise smooth motion flows by employing the tri-break
transform. We demonstrate the feasibility of utilising a common set
of breakpoints for compressing depth maps and motion fields anchored
Performing sound source separation and visual object segmentation jointly in naturally occurring videos is a notoriously difficult task, especially in the absence of annotated data. In this study, we leverage the concurrency between audio and visual modalities in an attempt to solve the joint audio-visual segmentation problem in a self-supervised manner. Human beings interact with the physical world through a few sensory systems such as vision, auditory, movement, etc. The usefulness of the interplay of such systems lies in the concept of degeneracy.
This paper investigates the effects of using video motion magnification methods based on amplitude and phase, respectively, to amplify small facial movements. We hypothesise that this approach will assist in the micro-expression recognition task. To this end, we apply the pre-trained VGGFace2 model with its excellent facial feature capturing ability to transfer learn the magnified micro-expression movement, then encode the spatial information and decode the spatial and temporal information by Bi-LSTM model.
Automated unsupervised video summarization by key-frame extraction consists in identifying representative video frames, best abridging a complete input sequence, and temporally ordering them to form a video summary, without relying on manually constructed ground-truth key-frame sets. State-of-the-art unsupervised deep neural approaches consider the desired summary to be a subset of the original sequence, composed of video frames that are sufficient to visually reconstruct the entire input.