Image processing strategies based on saliency segmentation for object recognition under simulated prosthetic vision

Li, Heng; Su, Xiaofan; Wang, Jing; Kan, Han; Han, Tingting; Zeng, Yajie; Chai, Xinyu

doi:10.1016/j.artmed.2017.11.001

Cited by 46 publications

(46 citation statements)

References 57 publications

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“…Wang et al [22] proposed two image representation strategies using background subtraction to segment moving elements for object recognition. Similarly, Guo et al [23] and Li et al [24] proposed two image processing strategies based on a saliency segmentation technique. For scene recognition, McCarthy et al [83] presented a visual representation based on intensity augments in order to emphasise regions of structural change.…”

Section: Actual/predictedmentioning

confidence: 99%

“…In terms of complex scene understanding, just few SPV studies have been proposed [24,84]. It is well established that in realistic environment, which is made of complex scenes, the observer is forced to select relevant elements [85].…”

Section: Actual/predictedmentioning

confidence: 99%

“…We evaluate and compare the proposed semantic and structural image segmentation with baseline methods through a Simulated Prosthetic Vision (SPV) experiment, which is a standard procedure for non-invasive evaluation using normal vision subjects [19][20][21][22][23][24][25][26][27][28][29][30][31][32][33][34][35][36]. The experiments included two tasks: object recognition and room identification.…”

Section: Introductionmentioning

confidence: 99%

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Semantic and structural image segmentation for prosthetic vision

2020

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Prosthetic vision is being applied to partially recover the retinal stimulation of visually impaired people. However, the phosphenic images produced by the implants have very limited information bandwidth due to the poor resolution and lack of color or contrast. The ability of object recognition and scene understanding in real environments is severely restricted for prosthetic users. Computer vision can play a key role to overcome the limitations and to optimize the visual information in the prosthetic vision, improving the amount of information that is presented. We present a new approach to build a schematic representation of indoor environments for simulated phosphene images. The proposed method combines a variety of convolutional neural networks for extracting and conveying relevant information about the scene such as structural informative edges of the environment and silhouettes of segmented objects. Experiments were conducted with normal sighted subjects with a Simulated Prosthetic Vision system. The results show good accuracy for object recognition and room identification tasks for indoor scenes using the proposed approach, compared to other image processing methods.

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Section: Actual/predictedmentioning

confidence: 99%

Section: Actual/predictedmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Semantic and structural image segmentation for prosthetic vision

2020

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“…The saliency detection models are based on the visual attention mechanism and are used to extract the salient features to generate the saliency map. Common models such as the Itti and the GBVS are widely used in the field of visual prosthesis [ 12 , 13 ]. However, the saliency map detected by the common model is discrete region [ 15 , 16 ].…”

Section: Image Processing Strategies Based On Salient Object Detecmentioning

confidence: 99%

“…Their results demonstrated that the saliency map can provide clues for searching and performing tasks for users with visual prosthesis. Wang et al [ 12 ] and Li et al [ 13 ] proposed two image processing strategies based on improved Itti and GBVS model to optimize the presentation in simulated prosthetic vision, respectively. Their results demonstrated that the use of the saliency segmentation method and image processing strategies can automatically extract and enhance foreground objects and significantly improve object recognition performance towards recipients with a high-density implant.…”

Section: Introductionmentioning

confidence: 99%

Optimization of Visual Information Presentation for Visual Prosthesis

Guo

Yang

Gao

2018

International Journal of Biomedical Imaging

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Visual prosthesis applying electrical stimulation to restore visual function for the blind has promising prospects. However, due to the low resolution, limited visual field, and the low dynamic range of the visual perception, huge loss of information occurred when presenting daily scenes. The ability of object recognition in real-life scenarios is severely restricted for prosthetic users. To overcome the limitations, optimizing the visual information in the simulated prosthetic vision has been the focus of research. This paper proposes two image processing strategies based on a salient object detection technique. The two processing strategies enable the prosthetic implants to focus on the object of interest and suppress the background clutter. Psychophysical experiments show that techniques such as foreground zooming with background clutter removal and foreground edge detection with background reduction have positive impacts on the task of object recognition in simulated prosthetic vision. By using edge detection and zooming technique, the two processing strategies significantly improve the recognition accuracy of objects. We can conclude that the visual prosthesis using our proposed strategy can assist the blind to improve their ability to recognize objects. The results will provide effective solutions for the further development of visual prosthesis.

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