We propose the ambiguity problem for the foreground object segmentation task and motivate the importance of estimating and accounting for this ambiguity when designing vision systems. Specifically, we distinguish between images which lead multiple annotators to segment different foreground objects (ambiguous) versus minor inter-annotator differences of the same object. Taking images from eight widely used datasets, we crowdsource labeling the images as "ambiguous" or "not ambiguous" to segment in order to construct a new dataset we call STATIC. Using STATIC, we develop a system that automatically predicts which images are ambiguous. Experiments demonstrate the advantage of our prediction system over existing saliency-based methods on images from vision benchmarks and images taken by blind people who are trying to recognize objects in their environment. Finally, we introduce a crowdsourcing system to achieve cost savings for collecting the diversity of all valid "ground truth" foreground object segmentations by collecting extra segmentations only when ambiguity is expected. Experiments show our system eliminates up to 47 of human effort compared to existing crowdsourcing methods with no loss in capturing the diversity of ground truths.
We study the problem of Salient Object Subitizing, i.e. predicting the existence and the number of salient objects in an image using holistic cues. This task is inspired by the ability of people to quickly and accurately identify the number of items within the subitizing range (1-4). To this end, we present a salient object subitizing image dataset of about 14K everyday images which are annotated using an online crowdsourcing marketplace. We show that using an end-to-end trained Convolutional Neural Network (CNN) model, we achieve prediction accuracy comparable to human performance in identifying images with zero or one salient object. For images with multiple salient objects, our model also provides significantly better than chance performance without requiring any localization process. Moreover, we propose a method to improve the training of the CNN subitizing model by leveraging synthetic images. In experiments, we demonstrate the accuracy and generalizability of our CNN subitizing model and its applications in salient object detection and image retrieval. Computer vision and human-powered services can provide blind people access to visual information in the world around them, but their efficacy is dependent on high-quality photo inputs. Blind people often have difficulty capturing the information necessary for these applications to work because they cannot see what they are taking a picture of. In this paper, we present Scan Search, a mobile application that offers a new way for blind people to take high-quality photos to support recognition tasks. To support realtime scanning of objects, we developed a key frame extraction algorithm that automatically retrieves high-quality frames from continuous camera video stream of mobile phones. Those key frames are streamed to a cloud-based recognition engine that identifies the most significant object inside the picture. This way, blind users can scan for objects of interest and hear potential results in real time. We also present a study exploring the tradeoffs in how many photos are sent, and conduct a user study with 8 blind participants that compares Scan Search with a standard photo-snapping interface. Our results show that Scan Search allows users to capture objects of interest more efficiently and is preferred by users to the standard interface.  Visual information pervades our environment. Vision is used to decide everything from what we want to eat at a restaurant and which bus route to take to whether our clothes match and how long until the milk expires. Individually, the inability to interpret such visual information is a nuisance for blind people who often have effective, if inefficient, work-arounds to overcome them. Collectively, however, they can make blind people less independent. Specialized technology addresses some problems in this space, but automatic approaches cannot yet answer the vast majority of visual questions that blind people may have. VizWiz addresses this shortcoming by using the Internet connections and cameras on existing smartphones to connect blind people and their questions to remote paid workers' answers. VizWiz is designed to have low latency and low cost, making it both competitive with expensive automatic solutions and much more versatile. Conventional saliency analysis methods measure the saliency of individual pixels. The resulting saliency map inevitably loses information in the original image and finding salient objects in it is difficult. We propose to detect salient objects by directly measuring the saliency of an image window in the original image and adopt the well established sliding window based object detection paradigm. We propose an assisted photography framework to help visually impaired users properly aim a camera and evaluate our implementation in the context of documenting public transportation accessibility. Our framework integrates user interaction during the image capturing process to help users take better pictures in real time. We use an image composition model to evaluate picture quality and suggest providing audiovisual feedback to improve users’ aiming position. With our particular framework implementation, blind participants were able to take pictures of similar quality to those taken by low vision participants without assistance. Likewise, our system helped low vision participants take pictures as good as those taken by fully sighted users. Our results also show a positive trend in favor of spoken directions to assist visually impaired users in comparison to tone and silent feedback. Positive usefulness ratings provided by full vision users further suggest that assisted photography has universal appeal. Blind people want to take photographs for the same reasons as others -- to record important events, to share experiences, and as an outlet for artistic expression. Furthermore, both automatic computer vision technology and human-powered services can be used to give blind people feedback on their environment, but to work their best these systems need high-quality photos as input. In this paper, we present the results of a large survey that shows how blind people are currently using cameras. Next, we introduce EasySnap, an application that provides audio feedback to help blind people take pictures of objects and people and show that blind photographers take better photographs with this feedback. We then discuss how we iterated on the portrait functionality to create a new application called PortraitFramer designed specifically for this function. Finally, we present the results of an in-depth study with 15 blind and low-vision participants, showing that they could pick up how to successfully use the application very quickly.