Collaterally cued labelling framework underpinning semantic-level visual content descriptor

In this paper, we introduce a novel high-level visual content descriptor devised for performing semantic-based image classification and retrieval. The work can be treated as an attempt for bridging the so called "semantic gap". The proposed image feature vector model is fundamentally underpinned by an automatic image labelling framework, called Collaterally Cued Labelling (CCL), which incorporates the collateral knowledge extracted from the collateral texts accompanying the images with the state-of-the-art low-level visual feature extraction techniques for automatically assigning textual keywords to image regions. A subset of the Corel image collection was used for evaluating the proposed method. The experimental results indicate that our semantic-level visual content descriptors outperform both conventional visual and textual image feature models.

Retrieval of global atmospheric electrical activity at a polluted urban site

The atmospheric electrical Potential Gradient (PG) arises from global thunderstorm activity, but surface measurements of the atmospheric Potential Gradient (PG) are influenced by global thunderstorms and local aerosol concentration changes. The local aerosol change can be monitored independently, and in some cases the concentration changes are closely related to PG changes. For these circumstances, a general theory to remove the local aerosol influence on PG measurements has been developed. Continuous measurements of PG and aerosol mass concentration were made during 24–31 Dec, 2005 within an urban environment at Reading, UK. The average diurnal variation of PG showed a double diurnal cycle, with maxima in the early morning and evening hours. The aerosol concentration has similar double maxima. Removing the aerosol using from the PG and aerosol correlation returns a single diurnal cycle, suggestive of the more global PG diurnal cycle.

A bridge-ship collision avoidance system based on FLIR image sequences

In this paper, a forward-looking infrared (FLIR) video surveillance system is presented for collision avoidance of moving ships to bridge piers. An image preprocessing algorithm is proposed to reduce clutter background by multi-scale fractal analysis, in which the blanket method is used for fractal feature computation. Then, the moving ship detection algorithm is developed from image differentials of the fractal feature in the region of surveillance between regularly interval frames. When the moving ships are detected in region of surveillance, the device for safety alert is triggered. Experimental results have shown that the approach is feasible and effective. It has achieved real-time and reliable alert to avoid collisions of moving ships to bridge piers.

Bayesian retrieval of complete posterior PDFs of oceanic rain rate from microwave observations

A new Bayesian algorithm for retrieving surface rain rate from Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) over the ocean is presented, along with validations against estimates from the TRMM Precipitation Radar (PR). The Bayesian approach offers a rigorous basis for optimally combining multichannel observations with prior knowledge. While other rain-rate algorithms have been published that are based at least partly on Bayesian reasoning, this is believed to be the first self-contained algorithm that fully exploits Bayes’s theorem to yield not just a single rain rate, but rather a continuous posterior probability distribution of rain rate. To advance the understanding of theoretical benefits of the Bayesian approach, sensitivity analyses have been conducted based on two synthetic datasets for which the “true” conditional and prior distribution are known. Results demonstrate that even when the prior and conditional likelihoods are specified perfectly, biased retrievals may occur at high rain rates. This bias is not the result of a defect of the Bayesian formalism, but rather represents the expected outcome when the physical constraint imposed by the radiometric observations is weak owing to saturation effects. It is also suggested that both the choice of the estimators and the prior information are crucial to the retrieval. In addition, the performance of the Bayesian algorithm herein is found to be comparable to that of other benchmark algorithms in real-world applications, while having the additional advantage of providing a complete continuous posterior probability distribution of surface rain rate.

Modified level set method with Canny operator for image noise removal

The level set method is commonly used to address image noise removal. Existing studies concentrate mainly on determining the speed function of the evolution equation. Based on the idea of a Canny operator, this letter introduces a new method of controlling the level set evolution, in which the edge strength is taken into account in choosing curvature flows for the speed function and the normal to edge direction is used to orient the diffusion of the moving interface. The addition of an energy term to penalize the irregularity allows for better preservation of local edge information. In contrast with previous Canny-based level set methods that usually adopt a two-stage framework, the proposed algorithm can execute all the above operations in one process during noise removal.

Image restoration for a rectangular poor-pixels detector

This paper presents a unique two-stage image restoration framework especially for further application of a novel rectangular poor-pixels detector, which, with properties of miniature size, light weight and low power consumption, has great value in the micro vision system. To meet the demand of fast processing, only a few measured images shifted up to subpixel level are needed to join the fusion operation, fewer than those required in traditional approaches. By maximum likelihood estimation with a least squares method, a preliminary restored image is linearly interpolated. After noise removal via Canny operator based level set evolution, the final high-quality restored image is achieved. Experimental results demonstrate effectiveness of the proposed framework. It is a sensible step towards subsequent image understanding and object identification.

Integrating features for man-made target tracking from FLIR image sequence using particle filter

A new man-made target tracking algorithm integrating features from (Forward Looking InfraRed) image sequence is presented based on particle filter. Firstly, a multiscale fractal feature is used to enhance targets in FLIR images. Secondly, the gray space feature is defined by Bhattacharyya distance between intensity histograms of the reference target and a sample target from MFF (Multi-scale Fractal Feature) image. Thirdly, the motion feature is obtained by differencing between two MFF images. Fourthly, a fusion coefficient can be automatically obtained by online feature selection method for features integrating based on fuzzy logic. Finally, a particle filtering framework is developed to fulfill the target tracking. Experimental results have shown that the proposed algorithm can accurately track weak or small man-made target in FLIR images with complicated background. The algorithm is effective, robust and satisfied to real time tracking.

Image fusion based on median filters and SOFM neural networks: a three-step scheme

This paper presents a new image data fusion scheme by combining median filtering with self-organizing feature map (SOFM) neural networks. The scheme consists of three steps: (1) pre-processing of the images, where weighted median filtering removes part of the noise components corrupting the image, (2) pixel clustering for each image using self-organizing feature map neural networks, and (3) fusion of the images obtained in Step (2), which suppresses the residual noise components and thus further improves the image quality. It proves that such a three-step combination offers an impressive effectiveness and performance improvement, which is confirmed by simulations involving three image sensors (each of which has a different noise structure).

Driving saccade to pursuit using image motion

Within the context of active vision, scant attention has been paid to the execution of motion saccades—rapid re-adjustments of the direction of gaze to attend to moving objects. In this paper we first develop a methodology for, and give real-time demonstrations of, the use of motion detection and segmentation processes to initiate capture saccades towards a moving object. The saccade is driven by both position and velocity of the moving target under the assumption of constant target velocity, using prediction to overcome the delay introduced by visual processing. We next demonstrate the use of a first order approximation to the segmented motion field to compute bounds on the time-to-contact in the presence of looming motion. If the bound falls below a safe limit, a panic saccade is fired, moving the camera away from the approaching object. We then describe the use of image motion to realize smooth pursuit, tracking using velocity information alone, where the camera is moved so as to null a single constant image motion fitted within a central image region. Finally, we glue together capture saccades with smooth pursuit, thus effecting changes in both what is being attended to and how it is being attended to. To couple the different visual activities of waiting, saccading, pursuing and panicking, we use a finite state machine which provides inherent robustness outside of visual processing and provides a means of making repeated exploration. We demonstrate in repeated trials that the transition from saccadic motion to tracking is more likely to succeed using position and velocity control, than when using position alone.

Quantifying the response of the ORAC aerosol optical depth retrieval for MSG SEVIRI to aerosol model assumptions

We test the response of the Oxford-RAL Aerosol and Cloud (ORAC) retrieval algorithm for MSG SEVIRI to changes in the aerosol properties used in the dust aerosol model, using data from the Dust Outflow and Deposition to the Ocean (DODO) flight campaign in August 2006. We find that using the observed DODO free tropospheric aerosol size distribution and refractive index increases simulated top of the atmosphere radiance at 0.55 µm assuming a fixed erosol optical depth of 0.5 by 10–15 %, reaching a maximum difference at low solar zenith angles. We test the sensitivity of the retrieval to the vertical distribution f the aerosol and find that this is unimportant in determining simulated radiance at 0.55 µm. We also test the ability of the ORAC retrieval when used to produce the GlobAerosol dataset to correctly identify continental aerosol outflow from the African continent and we find that it poorly constrains aerosol speciation. We develop spatially and temporally resolved prior distributions of aerosols to inform the retrieval which incorporates five aerosol models: desert dust, maritime, biomass burning, urban and continental. We use a Saharan Dust Index and the GEOS-Chem chemistry transport model to describe dust and biomass burning aerosol outflow, and compare AOD using our speciation against the GlobAerosol retrieval during January and July 2006. We find AOD discrepancies of 0.2–1 over regions of intense biomass burning outflow, where AOD from our aerosol speciation and GlobAerosol speciation can differ by as much as 50 - 70 %.

Self-centred memories: the reminiscence bump and the self

The self-memory relationship is thought to be bidirectional, in such a way that memories provide context for the self, and equally, the self exercises control over retrieval (Conway, 2005). Autobiographical memories are not distributed equally across the life span; instead, memories peak between ages 10 and 30. This reminiscence bump has been suggested to support the emergence of a stable and enduring self. In the present study, the relationship between memory accessibility and self was explored with a novel methodology that used generation of self images in the form of I am statements. Memories generated from I am cues clustered around the time of emergence for that particular self image. We argue that, when a new self-image is formed, it is associated with the encoding of memories that are relevant to that self and that remain highly accessible to the rememberer later in life. This study offers a new methodology for academics and clinicians interested in the relationship between memory and identity.