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David Gromov
David Gromov

Dynamic Alignment Through Imagery


"The use of imagery to improve human alignment and movement has been practiced by relatively few adherents, most of them professionals. Now, with Eric Franklin's book Dynamic Alignment Through Imagery, the technique of using imagery is made clear for the general public as well for professionals. Franklin is to be commended for bringing this important work to a wider audience."Andre BernardAdjunct Assistant Professor Dance EducationNew York University




Dynamic alignment through imagery



In this new edition, Franklin shows you how to use imagery, touch, and movement exercises to improve your coordination and alignment. These exercises will also help you relieve tension, enhance the health of your spine and back, and prevent back injury.


This book will help you discover your natural flexibility and quickly increase your power to move. You'll learn elements of body design. You'll explore how to use imagery to improve your confidence, and you'll discover imagery conditioning programs that will lead you toward better alignment, safer movement, increased fitness, and greater joy. Further, you'll examine how to apply this understanding to your discipline or training to improve your performance.


This book from Eric Franklin presents nearly 500 illustrated exercises - including numerous exercises that are set to music and available on the book's product page - to help you understand and achieve proper posture and alignment and release excess stress. The expanded second edition includes over 600 illustrations of anatomical imagery and updated chapter content. Illustrated. Softcover, 448 pages.


The second edition of Dynamic Alignment Through Imagery gives you the opportunity to listen to four audio recordings of imagery processes. Author Eric Franklin created these recordings to help increase the understanding of how imagery is used effectively and assist in embodying dynamic alignment. Following are the names of the recordings, their approximate run times, and brief descriptions:


Part I of Dynamic Alignment Through Imagery discusses the origins and uses of imagery and includes 36 exercises that demonstrate dynamic alignment in practice. You'll explore the importance of posture and dynamic alignment and discover how to use imagery to affect body movement.


Part III provides 250 anatomical imagery exercises to help you fine-tune alignments and increase body awareness. The exercises focus on different regions of the body--the pelvis, hips, knees, lower legs, spine, shoulders, arms, hands, head, and neck--as well as on breathing. You can select specific images to address individual needs or follow the sequence presented in the book.


And Part IV provides 23 holistic exercises to sculpt and improve alignment in various positions--standing, supine, and sitting. These exercises will help you establish a body image that facilitates dynamic alignment and releases excess tension.


Although the mechanisms of effect of imagery, and DNITM in particular, are not fully revealed to date (Callow et al., 2013), DNITM may be associated with not only practicing existing motor plans and habits (Willems et al., 2009) but actually refining and ameliorating them, thus resulting in enhanced motor execution, as was noticed following the intervention. Another potential explanation for the noticed effectiveness of the DNITM intervention may lie in its emphasis on kinesthetic imagery, which was suggested by previous literature to benefit motor performance (Lotze, 2013) and tasks that emphsize the relationship between various segments of the body (e.g., pelvis vs. thigh) (Shenton et al., 2004; Giron et al., 2012). Furthermore, the empahsis of the DNITM intervention on anatomical-proprioceptive awareness of the hip joint could specifically benefit developpé performance, given its suggested role in controlling pelvic alignment (Gamboian et al., 2000; Kiefer et al., 2013).


The significant increases in hip flexion and abduction ROM in all three tasks following the intervention may suggest, as part of a motor learning effect, an improved use of the hip joint through imagery, potentially resulting in more proper, effective motor plan (Debarnot et al., 2014) and function and increased embodiment of hip anatomy and biomechanics, all leading to better motor control over the pelvic-hip complex, thus increasing ROM.


This paper will describe a state-of-the-art approach to real-time wavefront sensing and image enhancement. It will explore Boeing's existing technology to realize a 50 Hz frame rate (with a path to 1 KHz and higher). At this higher rate, phase diversity will be readily applicable to compensate for distortions of large dynamic bandwidth such as those of the atmosphere. We will describe various challenges in aligning a two-camera phase diversity system. Such configurations make it almost impossible to process the captured images without additional upgrade in the algorithm to account for alignment errors. An example of an error is the relative misalignment of the two images, the "best-focus" and the diversity image, where it is extremely hard to maintain alignment to less than a fraction of 1 pixel. We will show that the algorithm performance increases dramatically when we account for these errors in the estimation process. Preliminary evaluation has assessed a National Imagery Interpretability Rating Scale increase of approximately 3 from the best-focus to the enhanced image. Such a performance improvement would greatly increase the operating range (or, equivalently, decrease the weight) of many optical systems.


In Chapter 4, it was argued that criticizing and evaluating an analogy relation is just as important a process as generating the analogous case in the first place. In other words, even if the analogous case is well understood and has yielded a prediction for the target problem, one must establish confidence in the validity of the analogy in order to have confidence in the prediction. I discussed a well-known strategy for analogy evaluation, that of mapping discrete features, and presented some evidence that experts exhibit it. I also introduced an additional evaluation strategy detected in the protocols called generating a bridging analogy. Now that there has been a discussion of imagery-based processes in Chapters 12 and 13, four more new analogy evaluation strategies discovered in the protocols can be introduced: conserving transformations, imagery alignment analogies (a special type of bridging analogy introduced in chapter 16), dual simulation comparisons used to detect perceptual/motor similarity, and overlay simulations (a special type of dual simulation). Accompanying the appearance of these strategies in the protocols are observations that strongly suggest the use of dynamic imagery. These suggest the methods involve imagery and may be imagery based. These findings add to previous evidence (Casakin and Goldschmidt, 1999; Clement, 1994, 2003; Craig, Nersessian and Catrambone, 2002; Croft and Thagard, 2002; Trickett and Trafton, 2002) for formulating the general hypothesis that many analogical reasoning processes can be imagery based. I will also discuss evidence for imagery being involved when subjects use transformations as a method for generating analogies, partitions, extreme cases, and explanatory models. Throughout this chapter, I use evidence from external subjects only.


Technical Abstract: It is important to assess the stand count of crops in the seedling stage for making proper field management decisions, such as replanting, to improve crop production. The conventional method of counting stand manually is time consuming and labor intensive, which makes it difficult to adequately cover a large field. Use of an unmanned aerial vehicle (UAV) as a high throughput tool could make this task more efficient. Cotton seedlings are small and not easily seen in UAV-based RGB images. Hyperspectral images could provide more information than common RGB images and help to distinguish cotton seedlings from soil. The goal of this study was to evaluate the potential of using a UAV-based pushbroom hyperspectral imaging system in cotton stand segmentation (i.e., separating the cotton seedling from the soil background in the images) and counting. A pushbroom hyperspectral camera covering the spectral range of 600 nm - 970 nm was integrated into a UAV platform to collect images of cotton seedlings at the altitude of 50 m above ground level. An image stitching and alignment algorithm including feature detection and matching, geometric transformation, dynamic panorama, and spectral band stitching, was developed to generate a panorama for each of the 103 bands. Vegetation indices were calculated using different bands and were used to remove soil background. A Hough transform was conducted for row identification and weed removal. The geometric characteristics of the identified objects in the images were used to estimate the cotton stand count. The number of cotton plants determined from the hyperspectral images was 2% less than the actual number (84.1% classification accuracy of stand count estimation in each segmented object in the binary images). Mean absolute percentage error (MAPE) = 9% was obtained for density and mean seedling space estimation, and MAPE = 6.8% was obtained for the seedling space standard deviation. The results showed that the UAV-based hyperspectral images had the potential to evaluate cotton stand count. The seedlings identified through the segmentation process will be used for evaluating plant vigor, uniformity and water stress detection in future studies. 041b061a72


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