Virtually Essence Effect Creator Prototype Development Effort- A Case Study


Zinkar Das,Himanshu Rai,Sudipta Ghosh ,Saswata Das ,Dipyaman Goswami ,Biswarup Neogi,



Essence effect,Internet technology,Odour,Image,Prototype,


Introducing modern transmission technology, it is possible to transmit some human sensual theme (sound, video, and picture) with support of signal processing aspects. It is quite difficult to transmit aroma introducing signal processing effort. We attempt to contribute a short prototype, which create a virtual effect of essence in receiving section. This paper mainly focuses with a case study manner towards the prototype development in techno commercial features. The specific patent review in this field is added it’s important. In addition, art work representation to working model based approaches is presented chronologically with appropriate technical information. Developed prototype and image processing technology behind this project is presented. The involvement of several interdisciplinary facts is carried towards the development of this prototype. Overall, this paper presents a case study towards the performance of one challenging product based preliminary prototype generation.


I. Anandan, P. (1985). Computing dense displacement fields with confidence measures in scenes containing occlusion.
II. Anandan, P. (1989). A computational framework and an algorithm for the measurement of visual motion. International Journal of Computer Vision, 2(3), 283-310.
III. Bodnar, A., Corbett, R., & Nekrasovski, D. (2004). AROMA: ambient awareness through olfaction in a messaging application. In Proceedings of the 6th international conference on Multimodal interfaces (pp. 183-190).
IV. Brown, M., & Lowe, D. G. (2002). Invariant Features from Interest Point Groups. In BMVC (No. s 1).
V. Brown, M., & Lowe, D. G. (2005). Unsupervised 3D object recognition and reconstruction in unordered datasets. In 3-D Digital Imaging and Modeling, 2005. 3DIM 2005. Fifth International Conference on (pp. 56-63).
VI. Brown, M., Szeliski, R., & Winder, S. (2005). Multi-image matching using multi-scale oriented patches. In Computer Vision and Pattern Recognition, 2005. CVPR 2005. IEEE Computer Society Conference on (Vol. 1, pp. 510-517).
VII. Brown, M., & Lowe, D. G. (2007). Automatic panoramic image stitching using invariant features. International journal of computer vision, 74(1), 59-73.
VIII. Das, Z., Manna, N., & Neogi, B. (2013). Model Representation and Study of Essence Effect Creation through Internet Technological Aspect. Innovative Systems Design and Engineering, 4(13), 25-33.

IX. E One Co. Ltd., “Perfume Emitting Device and Method”, KR20000023928, 2000.
X. Fawcett, T. (2006). An introduction to ROC analysis. Pattern recognition letters,27(8), 861-874.
XI. Gionis, A., Indyk, P., & Motwani, R. (1999). Similarity search in high dimensions via hashing. In VLDB (Vol. 99, pp. 518-529).
XII. Horn, B. K., & Schunck, B. G. (1981). Determining optical flow. International Society for Optics and Photonics.Technical symposium east (pp. 319-331).
XIII. Hua, G., Brown, M., & Winder, S. (2007). Discriminant embedding for local image descriptors. In Computer Vision, 2007. ICCV 2007. IEEE 11th International Conference on (pp. 1-8).
XIV. Jen, Y. H., Taha, Z., & Vui, L. J. (2008). VR-Based robot programming and simulation system for an industrial robot. International Journal of Industrial Engineering: Theory, Applications and Practice, 15(3), 314-322.
XV. Kulis, B., & Grauman, K. (2009). Kernelized locality-sensitive hashing for scalable image search. In Computer Vision, 2009 IEEE 12th International Conference on (pp. 2130-2137).
XVI. Lee C. (2001), “Aroma Distributor complementing internet operation is triggered by commandfrom audio decoder monitoring incoming data”, FR27971.
XVII. Lowe, D. G. (2004). Distinctive image features from scale-invariant keypoints. International journal of computer vision, 60(2), 91-110.
XVIII. Lucas, B. D., & Kanade, T. (1981). An iterative image registration technique with an application to stereo vision. In IJCAI (Vol. 81, pp. 674-679).
XIX. Mikolajczyk, K., & Schmid, C. (2004). Scale & affine invariant interest point detectors. International journal of computer vision, 60(1), 63-86.
XX. Okada, K., & Aiba, S. (2003). Toward the actualization of broadcasting service with smell information. Institute of Image information and Television Engineering of Japan Technical Report (in Japanese), 27(64), 31-34.
XXI. Raginsky, M., & Lazebnik, S. (2009). Locality-sensitive binary codes from shift-invariant kernels. In Advances in neural information processing systems (pp. 1509-1517).
XXII. Schmid, C., Mohr, R., & Bauckhage, C. (2000). Evaluation of interest point detectors. International Journal of computer vision, 37(2), 151-172.
XXIII. Shakhnarovich, G., Indyk, P., & Darrell, T. (2006). Nearest-neighbor methods in learning and vision: theory and practice.

XXIV. Shekar, A. (2012). Research-based enquiry in Product Development education: Lessons from supervising undergraduate final year projects. International Journal of Industrial Engineering: Theory, Applications and Practice. 19(1).
XXV. Shi, J., & Tomasi, C. (1994). Good features to track. In Computer Vision and Pattern Recognition, 1994. Proceedings CVPR’94., 1994 IEEE Computer Society Conference on (pp. 593-600).
XXVI. Snavely, N., Seitz, S. M., & Szeliski, R. (2006). Photo tourism: exploring photo collections in 3D. In ACM transactions on graphics (TOG) (Vol. 25, No. 3, pp. 835-846).
XXVII. Szeliski, R. (2010). Computer vision: algorithms and applications. Springer Science & Business Media.
XXVIII. Triggs, B. (2004). Detecting keypoints with stable position, orientation, and scale under illumination changes. Springer Berlin Heidelberg. In Computer Vision-ECCV 2004 (pp. 100-113).
XXIX. Torralba, A., Weiss, Y., & Fergus, R. (2008). Small codes and large databases of images for object recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
XXX. Yokoyama, S., Tanikawa, T., Hirota, K., & Hirose, M. (2004). Olfactory field simulation using wearable olfactory display. Trans. of Virtual Reality Society of Japan (in Japanese), 9(3), 265-274.

Author(s): Zinkar Das, Himanshu Rai, Sudipta Ghosh , Saswata Das , Dipyaman Goswami and Biswarup Neogi View Download